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1
Root, Benjamin, Paul Knight, George Young, Steven Greybush, Richard Grumm, Ron Holmes, and Jeremy Ross. "A Fingerprinting Technique for Major Weather Events." Journal of Applied Meteorology and Climatology 46, no. 7 (July 1, 2007): 1053–66. http://dx.doi.org/10.1175/jam2509.1.
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Abstract Advances in numerical weather prediction have occurred on numerous fronts, from sophisticated physics packages in the latest mesoscale models to multimodel ensembles of medium-range predictions. Thus, the skill of numerical weather forecasts continues to increase. Statistical techniques have further increased the utility of these predictions. The availability of large atmospheric datasets and faster computers has made pattern recognition of major weather events a feasible means of statistically enhancing the value of numerical forecasts. This paper examines the utility of pattern recognition in assisting the prediction of severe and major weather in the Middle Atlantic region. An important innovation in this work is that the analog technique is applied to NWP forecast maps as a pattern-recognition tool rather than to analysis maps as a forecast tool. A technique is described that employs a new clustering algorithm to objectively identify the anomaly patterns or “fingerprints” associated with past events. The potential refinement and applicability of this method as an operational forecasting tool employed by comparing numerical weather prediction forecasts with fingerprints already identified for major weather events are also discussed.
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N, Thushika, and Premaratne S. "A Data Mining Approach for Parameter Optimization in Weather Prediction." International Journal on Data Science 1, no. 1 (April 17, 2020): 1–13. http://dx.doi.org/10.18517/ijods.1.1.1-13.2020.
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More than two decades, there is a number of weather-related websites are available which approximately predict the weather and climate. By extracting essential data from the websites, a predictive data pattern can be produced to show the next day’s weather is with rain or not. By applying different types of web mining and analyzing techniques those extracted weather-related data can be visualized to a typical pattern for weather forecasting with the main deciding factors of weather. With the use of these approaches, reasonably precise forecasts can be made up to about four to five days in advance. For the weather prediction analysis, we need to discover deciding factors of the next day’s weather. Particularly, common weather dependent factors and the relationship of the prediction to the particular phenomenon. The solution proposed by this research can be used to analyze a large amount of weather data which are in different forms in each source. By using predictive mining task our solution allows us to make predictions for future instances according to the model what we have created. Evaluation measurements for the selected data mining technique such as accuracy percentage, TP & FP Rate, Precision, F-Measure, ROC area, SSE, and loglikelihood for classification and clustering leads to create a high-quality model of prediction. Knowledge flow interface provides the data flow to show the processing and analyzing data with precise association rules. In order to evaluate the model, SSE values and time to build the model, are considered in an effective manner.
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Nigro, Melissa A., John J. Cassano, and Mark W. Seefeldt. "A Weather-Pattern-Based Approach to Evaluate the Antarctic Mesoscale Prediction System (AMPS) Forecasts: Comparison to Automatic Weather Station Observations." Weather and Forecasting 26, no. 2 (April 1, 2011): 184–98. http://dx.doi.org/10.1175/2010waf2222444.1.
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Abstract Typical model evaluation strategies evaluate models over large periods of time (months, seasons, years, etc.) or for single case studies such as severe storms or other events of interest. The weather-pattern-based model evaluation technique described in this paper uses self-organizing maps to create a synoptic climatology of the weather patterns present over a region of interest, the Ross Ice Shelf for this analysis. Using the synoptic climatology, the performance of the model, the Weather Research and Forecasting Model run within the Antarctic Mesoscale Prediction System, is evaluated for each of the objectively identified weather patterns. The evaluation process involves classifying each model forecast as matching one of the weather patterns from the climatology. Subsequently, statistics such as model bias, root-mean-square error, and correlation are calculated for each weather pattern. This allows for the determination of model errors as a function of weather pattern and can highlight if certain errors occur under some weather regimes and not others. The results presented in this paper highlight the potential benefits of this new weather-pattern-based model evaluation technique.
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Rew, Jehyeok, Sungwoo Park, Yongjang Cho, Seungwon Jung, and Eenjun Hwang. "Animal Movement Prediction Based on Predictive Recurrent Neural Network." Sensors 19, no. 20 (October 11, 2019): 4411. http://dx.doi.org/10.3390/s19204411.
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Observing animal movements enables us to understand animal behavior changes, such as migration, interaction, foraging, and nesting. Based on spatiotemporal changes in weather and season, animals instinctively change their position for foraging, nesting, or breeding. It is known that moving patterns are closely related to their traits. Analyzing and predicting animals’ movement patterns according to spatiotemporal change offers an opportunity to understand their unique traits and acquire ecological insights into animals. Hence, in this paper, we propose an animal movement prediction scheme using a predictive recurrent neural network architecture. To do that, we first collect and investigate geo records of animals and conduct pattern refinement by using random forest interpolation. Then, we generate animal movement patterns using the kernel density estimation and build a predictive recurrent neural network model to consider the spatiotemporal changes. In the experiment, we perform various predictions using 14 K long-billed curlew locations that contain their five-year movements of the breeding, non-breeding, pre-breeding, and post-breeding seasons. The experimental results confirm that our predictive model based on recurrent neural networks can be effectively used to predict animal movement.
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Richardson, Doug, Hayley J. Fowler, Christopher G. Kilsby, Robert Neal, and Rutger Dankers. "Improving sub-seasonal forecast skill of meteorological drought: a weather pattern approach." Natural Hazards and Earth System Sciences 20, no. 1 (January 14, 2020): 107–24. http://dx.doi.org/10.5194/nhess-20-107-2020.
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Abstract. Dynamical model skill in forecasting extratropical precipitation is limited beyond the medium-range (around 15 d), but such models are often more skilful at predicting atmospheric variables. We explore the potential benefits of using weather pattern (WP) predictions as an intermediary step in forecasting UK precipitation and meteorological drought on sub-seasonal timescales. Mean sea-level pressure forecasts from the European Centre for Medium-Range Weather Forecasts ensemble prediction system (ECMWF-EPS) are post-processed into probabilistic WP predictions. Then we derive precipitation estimates and dichotomous drought event probabilities by sampling from the conditional distributions of precipitation given the WPs. We compare this model to the direct precipitation and drought forecasts from the ECMWF-EPS and to a baseline Markov chain WP method. A perfect-prognosis model is also tested to illustrate the potential of WPs in forecasting. Using a range of skill diagnostics, we find that the Markov model is the least skilful, while the dynamical WP model and direct precipitation forecasts have similar accuracy independent of lead time and season. However, drought forecasts are more reliable for the dynamical WP model. Forecast skill scores are generally modest (rarely above 0.4), although those for the perfect-prognosis model highlight the potential predictability of precipitation and drought using WPs, with certain situations yielding skill scores of almost 0.8 and drought event hit and false alarm rates of 70 % and 30 %, respectively.
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Guo, Jia, Teng Li, Rong Cheng, and Lingfeng Tan. "Research on weather classification pattern recognition based on support vector machine." E3S Web of Conferences 218 (2020): 04023. http://dx.doi.org/10.1051/e3sconf/202021804023.
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weather is the most important factor affecting the photovoltaic power generation.In this paper, the irradiance data of a photovoltaic power station in crodora in 2020 are collected, and the daily out of ground irradiance and the measured irradiance curve of that day are compared and observed, then the weather of that year is classified by human work, and then the daily irradiance data records are counted for the relevant indicators, with the maximum third order Based on the attributes of difference value, discrete difference and normalized variance, it is unified with the classified weather type.Then, the SVM prediction model of weather category is established based on radial basis function, and the optimal model parameters are determined by cross validation, so that a large number of historical date weather categories can be classified and predicted.This is obviously different from the traditional prediction method based on linear statistical theory, and the results show that it has a good effect.
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Lin, Yisha, Zongxiang Lu, Ying Qiao, Mingjie Li, and Zhifeng Liang. "Medium and long-term wind energy forecasting method considering multi-scale periodic pattern." E3S Web of Conferences 182 (2020): 01002. http://dx.doi.org/10.1051/e3sconf/202018201002.
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Medium and long-term weather sequence forecast becomes unreliable beyond two weeks since the weather is a chaotic system. Using values of same months for electricity prediction of wind power is the usual method. This approach defaults wind power output with annual cycle law. However, the periodic pattern can be very complicated in fact with multiple time scales. This paper proposes an approach with multi-scale periodic pattern considered. The application of parametric estimation on cumulative distribution function avoids the difficulty of predicting the power curve. Meteorological condition is considered to some extent via multi-scale periodic pattern explored basing on historical energy data. This work is an exploration for medium and long-term wind power forecasting that can well adapt to existing conditions. It has better prediction accuracy than the method without multi-scale periodicity considered.
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Tyagi, Himani, Shweta Suran, and Vishwajeet Pattanaik. "Weather - Temperature Pattern Prediction and Anomaly Identification using Artificial Neural Network." International Journal of Computer Applications 140, no. 3 (April 15, 2016): 15–21. http://dx.doi.org/10.5120/ijca2016909252.
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Gristey, Jake J., J. Christine Chiu, Robert J. Gurney, Cyril J. Morcrette, Peter G. Hill, Jacqueline E. Russell, and Helen E. Brindley. "Insights into the diurnal cycle of global Earth outgoing radiation using a numerical weather prediction model." Atmospheric Chemistry and Physics 18, no. 7 (April 16, 2018): 5129–45. http://dx.doi.org/10.5194/acp-18-5129-2018.
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Abstract. A globally complete, high temporal resolution and multiple-variable approach is employed to analyse the diurnal cycle of Earth's outgoing energy flows. This is made possible via the use of Met Office model output for September 2010 that is assessed alongside regional satellite observations throughout. Principal component analysis applied to the long-wave component of modelled outgoing radiation reveals dominant diurnal patterns related to land surface heating and convective cloud development, respectively explaining 68.5 and 16.0 % of the variance at the global scale. The total variance explained by these first two patterns is markedly less than previous regional estimates from observations, and this analysis suggests that around half of the difference relates to the lack of global coverage in the observations. The first pattern is strongly and simultaneously coupled to the land surface temperature diurnal variations. The second pattern is strongly coupled to the cloud water content and height diurnal variations, but lags the cloud variations by several hours. We suggest that the mechanism controlling the delay is a moistening of the upper troposphere due to the evaporation of anvil cloud. The short-wave component of modelled outgoing radiation, analysed in terms of albedo, exhibits a very dominant pattern explaining 88.4 % of the variance that is related to the angle of incoming solar radiation, and a second pattern explaining 6.7 % of the variance that is related to compensating effects from convective cloud development and marine stratocumulus cloud dissipation. Similar patterns are found in regional satellite observations, but with slightly different timings due to known model biases. The first pattern is controlled by changes in surface and cloud albedo, and Rayleigh and aerosol scattering. The second pattern is strongly coupled to the diurnal variations in both cloud water content and height in convective regions but only cloud water content in marine stratocumulus regions, with substantially shorter lag times compared with the long-wave counterpart. This indicates that the short-wave radiation response to diurnal cloud development and dissipation is more rapid, which is found to be robust in the regional satellite observations. These global, diurnal radiation patterns and their coupling with other geophysical variables demonstrate the process-level understanding that can be gained using this approach and highlight a need for global, diurnal observing systems for Earth outgoing radiation in the future.
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Shaukat, Muhammad Haroon, Ijaz Hussain, Muhammad Faisal, Ahmad Al-Dousari, Muhammad Ismail, Alaa Mohamd Shoukry, Elsayed Elsherbini Elashkar, and Showkat Gani. "Monthly drought prediction based on ensemble models." PeerJ 8 (September 8, 2020): e9853. http://dx.doi.org/10.7717/peerj.9853.
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Drought is a natural hazard, which is a result of a prolonged shortage of precipitation, high temperature and change in the weather pattern. Drought harms society, the economy and the natural environment, but it is difficult to identify and characterize. Many areas of Pakistan have suffered severe droughts during the last three decades due to changes in the weather pattern. A drought analysis with the incorporation of climate information has not yet been undertaken in this study region. Here, we propose an ensemble approach for monthly drought prediction and to define and examine wet/dry events. Initially, the drought events were identified by the short term Standardized Precipitation Index (SPI-3). Drought is predicted based on three ensemble models i.e., Equal Ensemble Drought Prediction (EEDP), Weighted Ensemble Drought Prediction (WEDP) and the Conditional Ensemble Drought Prediction (CEDP) model. Besides, two weighting procedures are used for distributing weights in the WEDP model, such as Traditional Weighting (TW) and the Weighted Bootstrap Resampling (WBR) procedure. Four copula families (i.e., Frank, Clayton, Gumbel and Joe) are used to explain the dependency relation between climate indices and precipitation in the CEDP model. Among all four copula families, the Joe copula has been found suitable for most of the times. The CEDP model provides better results in terms of accuracy and uncertainty as compared to other ensemble models for all meteorological stations. The performance of the CEDP model indicates that the climate indices are correlated with a weather pattern of four meteorological stations. Moreover, the percentage occurrence of extreme drought events that have appeared in the Multan, Bahawalpur, Barkhan and Khanpur are 1.44%, 0.57%, 2.59% and 1.71%, respectively, whereas the percentage occurrence of extremely wet events are 2.3%, 1.72%, 0.86% and 2.86%, respectively. The understanding of drought pattern by including climate information can contribute to the knowledge of future agriculture and water resource management.
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Wei, Chih-Chiang, and Chen-Chia Hsu. "Real-Time Rainfall Forecasts Based on Radar Reflectivity during Typhoons: Case Study in Southeastern Taiwan." Sensors 21, no. 4 (February 18, 2021): 1421. http://dx.doi.org/10.3390/s21041421.
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This study developed a real-time rainfall forecasting system that can predict rainfall in a particular area a few hours before a typhoon’s arrival. The reflectivity of nine elevation angles obtained from the volume coverage pattern 21 Doppler radar scanning strategy and ground-weather data of a specific area were used for accurate rainfall prediction. During rainfall prediction and analysis, rainfall retrievals were first performed to select the optimal radar scanning elevation angle for rainfall prediction at the current time. Subsequently, forecasting models were established using a single reflectivity and all elevation angles (10 prediction submodels in total) to jointly predict real-time rainfall and determine the optimal predicted values. This study was conducted in southeastern Taiwan and included three onshore weather stations (Chenggong, Taitung, and Dawu) and one offshore weather station (Lanyu). Radar reflectivities were collected from Hualien weather surveillance radar. The data for a total of 14 typhoons that affected the study area in 2008–2017 were collected. The gated recurrent unit (GRU) neural network was used to establish the forecasting model, and extreme gradient boosting and multiple linear regression were used as the benchmarks. Typhoons Nepartak, Meranti, and Megi were selected for simulation. The results revealed that the input data set merged with weather-station data, and radar reflectivity at the optimal elevation angle yielded optimal results for short-term rainfall forecasting. Moreover, the GRU neural network can obtain accurate predictions 1, 3, and 6 h before typhoon occurrence.
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Kim, Yong-Hyuk, and Yourim Yoon. "Spatiotemporal Pattern Networks of Heavy Rain among Automatic Weather Stations and Very-Short-Term Heavy-Rain Prediction." Advances in Meteorology 2016 (2016): 1–13. http://dx.doi.org/10.1155/2016/4063632.
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Spatiotemporal pattern networks of heavy rain among automatic weather stations, which reflect the mobility of heavy rain, were constructed and analyzed based on the hourly precipitation data over the last ten years (from 2003 to 2012) in South Korea. Moreover, a new algorithm applying the constructed heavy-rain pattern networks to very-short-term heavy-rain prediction was developed, and significant prediction results could be obtained.
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Dai, Panxi, and Benkui Tan. "The Nature of the Arctic Oscillation and Diversity of the Extreme Surface Weather Anomalies It Generates." Journal of Climate 30, no. 14 (July 2017): 5563–84. http://dx.doi.org/10.1175/jcli-d-16-0467.1.
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Through a cluster analysis of daily NCEP–NCAR reanalysis data, this study demonstrates that the Arctic Oscillation (AO), defined as the leading empirical orthogonal function (EOF) of 250-hPa geopotential height anomalies, is not a unique pattern but a continuum that can be well approximated by five discrete, representative AO-like patterns. These AO-like patterns grow simultaneously from disturbances in the North Pacific, the North Atlantic, and the Arctic, and both the feedback from the high-frequency eddies in the North Pacific and North Atlantic and propagation of the low-frequency wave trains from the North Pacific across North America into the North Atlantic play important roles in the pattern formation. Furthermore, it is shown that the structures and frequencies of occurrence of the five AO-like patterns are significantly modulated by El Niño–Southern Oscillation (ENSO). Warm (cold) ENSO enhances the negative (positive) AO phase, compared with ENSO neutral winters. Finally, the surface weather effects of these AO-like patterns and their implications for the AO-related weather prediction and the AO-North Atlantic Oscillation (NAO) relationship are discussed.
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Rawat, Shraddha, R. K. Singh, and A. S. Nain. "Analyzing Spatial Pattern of Weather Induced Yield Variability in Indian Mustard for Formation of Homogeneous Zones in North Western Himalaya and Indo-Gangetic Plains of India." Current Agriculture Research Journal 6, no. 3 (December 25, 2018): 278–85. http://dx.doi.org/10.12944/carj.6.3.07.
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Yield prediction plays an important role to decide the economy of farmer as well as the country. It avoids the under and over cropping of the particular crop. The production of not only mustard crop but all the agricultural crops is mainly affected by the weather variables. The changing weather condition affects the growth and development of crop causing intra seasonal yield variability. In addition, with weather variations, the spatial variability and crop management practices also plays a decisive role. As a result, yield forecasting represents an important tool for optimizing crop yield and to evaluate the crop-area insurance contracts. Considering yield variability and importance of rapeseed-mustard for farmers an attempt has been made to develop a homogeneous zone in respect to inter annual weather induced variability with help of this large region yield prediction could be done easily. For this study the 33 districts of erstwhile Uttar Pradesh has been selected and rapeseed-mustard data have been collected for the year 1997-2016. In this study a three steps approach has been adopted;1) the prediction of trend yield, 2) estimation of yield deviation and cluster formation and 3) mapping of the clusters in GIS and creation of homogeneous zones. Then these homogeneous zones created on basis of weather induced variability were used for yield forecasting of mustard in this region.
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Nayak, Munir Ahmad, and Subimal Ghosh. "Prediction of extreme rainfall event using weather pattern recognition and support vector machine classifier." Theoretical and Applied Climatology 114, no. 3-4 (March 6, 2013): 583–603. http://dx.doi.org/10.1007/s00704-013-0867-3.
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Núñez, M., R. Fidalgo, M. Baena, and R. Morales. "The influence of active region information on the prediction of solar flares: an empirical model using data mining." Annales Geophysicae 23, no. 9 (November 22, 2005): 3129–38. http://dx.doi.org/10.5194/angeo-23-3129-2005.
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Abstract. Predicting the occurrence of solar flares is a challenge of great importance for many space weather scientists and users. We introduce a data mining approach, called Behavior Pattern Learning (BPL), for automatically discovering correlations between solar flares and active region data, in order to predict the former. The goal of BPL is to predict the interval of time to the next solar flare and provide a confidence value for the associated prediction. The discovered correlations are described in terms of easy-to-read rules. The results indicate that active region dynamics is essential for predicting solar flares.
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Sun, Xia, Lian Xie, Fredrick Semazzi, and Bin Liu. "Effect of Lake Surface Temperature on the Spatial Distribution and Intensity of the Precipitation over the Lake Victoria Basin." Monthly Weather Review 143, no. 4 (March 31, 2015): 1179–92. http://dx.doi.org/10.1175/mwr-d-14-00049.1.
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Abstract A series of sensitivity experiments are performed to investigate the response of precipitation over the Lake Victoria basin (LVB) to the changes of lake surface temperature (LST) using the Weather Research and Forecasting (WRF) Model. It is shown that the default LST initialized from NCEP FNL (Final) Operational Global Analysis is deficient for simulating the rainfall over the LVB. Comparative experiments demonstrate the unambiguous impact of LST on the intensity and pattern of the precipitation over LVB. Intensification/weakening of precipitation over the lake occur with increasing/decreasing LST for both uniform and asymmetrical LST distribution. However, the relationship between rainfall anomalies and LST variations is nonlinear. Replacing the LST directly derived from global weather forecast models by the mean area-averaged LST of Lake Victoria (approximately 24°C) leads to improved rainfall simulation. However, LST with realistic cross-basin gradient is necessary to obtain a rainfall pattern consistent with the observations. The fact that rainfall and wind patterns over the lake are sensitive to LST distribution suggests the need to monitor the mesoscale LST pattern for accurate weather and climate prediction over LVB. It is also found that although the LST distribution exerts significant impact on the observed rainfall pattern, the area and location of the rainband are quite persistent under different LST forcing. This suggests that although the details of the rainfall pattern over LVB are strongly influenced by LST, the broad rainfall pattern is likely controlled by the atmospheric circulation and orography in the region.
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Zhang, Yu Long, and Jian Zhang. "Rainfall Characteristics Joint Regionalization Based on MST-Clustering Algorithm for Rain Attenuation Prediction." Applied Mechanics and Materials 543-547 (March 2014): 1694–97. http://dx.doi.org/10.4028/www.scientific.net/amm.543-547.1694.
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Rain attenuation prediction model of earth-satellite link, based on numerical weather forecast (NMF) statistics, require regionalization synthesizing the precipitation, rain-top height , and rainfall pattern (or time distribution) information, which ITU regionalization ignored. Thus, based on theMST-Clustering regionalization of average precipitation,themean delta binary(MDB)codingis presented to record rainfall pattern information, andRainfall Characteristics Joint Regionalizationis put forward to combine the precipitation and rain-top information. Finally, on the basis of Chinese meteorological monthly statistics, theRainfall Characteristics Region Mapis given, which is the essential step in the rain attenuation prediction model based on NMF statistics.
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Black, Jiaxin, Nathaniel C. Johnson, Stephen Baxter, Steven B. Feldstein, Daniel S. Harnos, and Michelle L. L’Heureux. "The Predictors and Forecast Skill of Northern Hemisphere Teleconnection Patterns for Lead Times of 3–4 Weeks." Monthly Weather Review 145, no. 7 (July 2017): 2855–77. http://dx.doi.org/10.1175/mwr-d-16-0394.1.
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The Pacific–North American pattern (PNA), North Atlantic Oscillation (NAO), and Arctic Oscillation (AO) are three dominant teleconnection patterns known to strongly affect December–February surface weather in the Northern Hemisphere. A partial least squares regression (PLSR) method is adopted in this study to generate wintertime two-week statistical forecasts of these three teleconnection pattern indices for lead times of up to five weeks over the 1980–2013 period. The PLSR approach generates forecasts for the teleconnection pattern indices by maximizing the variance explained by predictor indices determined as linear combinations of predictor fields, which include gridded outgoing longwave radiation (OLR), 300-hPa geopotential height (Z300), and 50-hPa geopotential height (Z50). Overall, the PLSR models yield statistically significant skill at all lead times up to five weeks. In particular, cross-validated correlations between the combined weeks 3–4 PLSR forecasts and verification for the PNA, NAO, and AO indices are 0.34, 0.28, and 0.41, respectively. The PLSR approach also allows the authors to isolate a small number of predictor patterns that help shed light on the sources of prediction skill for each teleconnection pattern. As expected, the results reveal the importance of tropical convection (OLR) for forecast skill in weeks 3–4, but the initial atmospheric flow (Z300) accounts for a substantial fraction of the skill as well. Overall, the results of this study provide promise for improving subseasonal-to-seasonal (S2S) forecasts and the physical understanding of predictability on these time scales.
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Khasanah, Farida Nur, and Fhira Nhita. "Weather Forecasting in Bandung Regency based on FP-Growth Algorithm." International Journal on Information and Communication Technology (IJoICT) 4, no. 2 (April 2, 2019): 1. http://dx.doi.org/10.21108/ijoict.2018.42.203.
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<p>Weather change is one of the things that can affect people around the world in doing activities, including in Indonesia. The area of Indonesia, especially in Bandung regency has a high intensity of rainfall, compared with other regions. The people of Bandung Regency mostly have livelihoods in the fields of industry and agriculture, both of which are closely related to the effects of weather. Weather prediction is used for reference, so the future of society can prepare all possible weather before the move. One method of data mining used to predict weather is the association rule method. In this method there is Frequent Pattern Growth (FP-Growth) algorithm, this algorithm is used to determine the pattern of linkage between attribute weather with rainfall. The result of the FP-Growth algorithm is an association rule, the result of the algorithm rules is then used as reference for data entry in the classification process, where the process is done to get the forecast based on the rainfall category to obtain maximum accuracy. The highest performance result of FP-Growth from the result of rules based on its confidence value is 92%.</p>
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Chao, Zeyi, Fangling Pu, Yuke Yin, Bin Han, and Xiaoling Chen. "Research on Real-Time Local Rainfall Prediction Based on MEMS Sensors." Journal of Sensors 2018 (June 26, 2018): 1–9. http://dx.doi.org/10.1155/2018/6184713.
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A more accurate and timely rainfall prediction is needed for flood disaster reduction and prevention in Wuhan. The in situ microelectromechanical systems’ (MEMS) sensors can provide high time and spatial resolution of weather parameter measurement, but they suffer from stochastic measurement error. In order to apply MEMS sensors in real-time rainfall prediction in Wuhan, firstly, seasonal trend decomposition using Loess (STL) algorithm is utilized to decompose the observed time series into trend, seasonal, and remainder components. The trend of the observed series is compared with the corresponding trend of the data downloaded from the authoritative website with the same weather parameter in terms of Euclidean distance and cosine similarity. The similarity demonstrates that the observation of MEMS sensors is believable. Secondly, the long short-term memory (LSTM) is used to predict the real-time rainfall based on the observed data. Compared with autoregressive and moving average (ARMA), random forest (RF), support vector machine (SVM), and back propagation neural networks (BPNNs), LSTM not only performs as well as ARMA in real-time rainfall prediction but also outperforms the other four models in seasonal rainfall pattern description and seasonal real-time rainfall prediction. Our experiment results show that more detailed, timely, and accurate rainfall prediction can be achieved by using LSTM on the MEMS weather sensors.
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Chen, Zhen, and Wei Fan. "Data analytics approach for travel time reliability pattern analysis and prediction." Journal of Modern Transportation 27, no. 4 (September 12, 2019): 250–65. http://dx.doi.org/10.1007/s40534-019-00195-6.
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Abstract Travel time reliability (TTR) is an important measure which has been widely used to represent the traffic conditions on freeways. The objective of this study is to develop a systematic approach to analyzing TTR on roadway segments along a corridor. A case study is conducted to illustrate the TTR patterns using vehicle probe data collected on a freeway corridor in Charlotte, North Carolina. A number of influential factors are considered when analyzing TTR, which include, but are not limited to, time of day, day of week, year, and segment location. A time series model is developed and used to predict the TTR. Numerical results clearly indicate the uniqueness of TTR patterns under each case and under different days of week and weather conditions. The research results can provide insightful and objective information on the traffic conditions along freeway segments, and the developed data-driven models can be used to objectively predict the future TTRs, and thus to help transportation planners make informed decisions.
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Flores, R. A. A. "GEOVISUAL ANALYTICS ON THE VERIFICATION OF THE PAGASA OPERATIONAL NUMERICAL WEATHER PREDICTION MODEL RAINFALL FORECAST." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W19 (December 23, 2019): 215–22. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w19-215-2019.
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Abstract. Assessment of NWP model performance is an integral part of operational forecasting as well as in research and development. Understanding the bias propagation of an NWP model and how it propagates across space can provide more insight in determining underlying causes and weaknesses not easily determined in traditional methods. The study aims to introduce the integration of the spatial distribution of error in interpreting model verification results by assessing how well the operational numerical weather prediction system of PAGASA captures the country’s weather pattern in each of its climate type. It also discusses improvements in model performance throughout the time-frame of analysis. Error propagation patterns were identified using Geovisual Analytics to allow comparison of verification scores among individual stations. The study concluded that a major update in the physics parameterization of the model in 2016 and continued minor updates in the following years, surface precipitation forecasts greatly improved from an average RMSE of 9.3, MAE of 3.2 and Bias of 1.36 in 2015 to an RMSE of 7.9, MAE of 2.5 and bias of −0.63 in 2018.
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Tseng, Kai-Chih, Eric Maloney, and Elizabeth A. Barnes. "The Consistency of MJO Teleconnection Patterns on Interannual Time Scales." Journal of Climate 33, no. 9 (May 1, 2020): 3471–86. http://dx.doi.org/10.1175/jcli-d-19-0510.1.
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AbstractThe Madden–Julian oscillation (MJO) excites strong variations in extratropical geopotential heights that modulate extratropical weather, making the MJO an important predictability source on subseasonal to seasonal time scales (S2S). Previous research demonstrates a strong similarity of teleconnection patterns across MJO events for certain MJO phases (i.e., pattern consistency) and increased model ensemble agreement during these phases that is beneficial for extended numerical weather forecasts. However, the MJO’s ability to modulate extratropical weather varies greatly on interannual time scales, which brings extra uncertainty in leveraging the MJO for S2S prediction. Few studies have investigated the mechanisms responsible for variations in the consistency of MJO tropical–extratropical teleconnections on interannual time scales. This study uses reanalysis data, ensemble simulations of a linear baroclinic model, and a Rossby wave ray tracing algorithm to demonstrate that two mechanisms largely determine the interannual variability of MJO teleconnection consistency. First, the meridional shift of stationary Rossby wave ray paths indicates increases (decreases) in the MJO’s extratropical modulation during La Niña (El Niño) years. Second, a previous study proposed that the constructive interference of Rossby wave signals caused by a dipole Rossby wave source pattern across the subtropical jet during certain MJO phases produces a consistent MJO teleconnection. However, this dipole feature is less clear in both El Niño and La Niña years due to the extension and contraction of MJO convection, respectively, which would decrease the MJO’s influence in the extratropics. Hence, considering the joint influence of the basic state and MJO forcing, this study suggests a diminished potential to leverage the MJO for S2S prediction in El Niño years.
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Xu, Jing Wen, Jun Fang Zhao, Wan Chang Zhang, and Xiao Xun Xu. "A Novel Soil Moisture Predicting Method Based on Artificial Neural Network and Xinanjiang Model." Advanced Materials Research 121-122 (June 2010): 1028–32. http://dx.doi.org/10.4028/www.scientific.net/amr.121-122.1028.
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Soil moisture plays an important role in agricultural drought predicting, therefore there is an increasing demand for detailed predictions of soil moisture, especially at basin scales. However, so far soil moisture predictions are usually obtained as a by-product of climate and weather prediction models coupled with a land surface parameterization scheme, and there has been little dedicated work to meet this urgent need at basin scales. In order to improve the basin hydrological models’ performance in the soil moisture forecasting, an integrated soil moisture predicting model based on Artificial Neural Network (ANN) and Xinanjiang model is proposed and presented in this paper. The performance of the new integrated soil moisture predicting model was tested in the Linyi watershed with a drainage area of 10040 km2, located in the semi-arid area of the eastern China. The results suggest that the soil moisture simulated by the integrated ANN-Xinanjiang model is more agree with the observed ones than that simulated by Xinanjiang, and that the simulated soil moisture by both the models has the similar trend and temporal change pattern with the observed one.
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Khan, Md Nasim, and Mohamed M. Ahmed. "Snow Detection using In-Vehicle Video Camera with Texture-Based Image Features Utilizing K-Nearest Neighbor, Support Vector Machine, and Random Forest." Transportation Research Record: Journal of the Transportation Research Board 2673, no. 8 (April 17, 2019): 221–32. http://dx.doi.org/10.1177/0361198119842105.
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Snowfall negatively affects pavement and visibility conditions, making it one of the major causes of motor vehicle crashes in winter weather. Therefore, providing drivers with real-time roadway weather information during adverse weather is crucial for safe driving. Although road weather stations can provide weather information, these stations are expensive and often do not represent real-time trajectory-level weather information. The main motivation of this study was to develop an affordable in-vehicle snow detection system which can provide trajectory-level weather information in real time. The system utilized SHRP2 Naturalistic Driving Study video data and was based on machine learning techniques. To train the snow detection models, two texture-based image features including gray level co-occurrence matrix (GLCM) and local binary pattern (LBP), and three classification algorithms: support vector machine (SVM), k-nearest neighbor (K-NN), and random forest (RF) were used. The analysis was done on an image dataset consisting of three weather conditions: clear, light snow, and heavy snow. While the highest overall prediction accuracy of the models based on the GLCM features was found to be around 86%, the models considering the LBP based features provided a much higher prediction accuracy of 96%. The snow detection system proposed in this study is cost effective, does not require a lot of technical support, and only needs a single video camera. With the advances in smartphone cameras, simple mobile apps with proper data connectivity can effectively be used to detect roadway weather conditions in real time with reasonable accuracy.
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Fan, Hongdou, Lin Wang, Yang Zhang, Youmin Tang, Wansuo Duan, and Lei Wang. "Predictable Patterns of Wintertime Surface Air Temperature in Northern Hemisphere and Their Predictability Sources in the SEAS5." Journal of Climate 33, no. 24 (December 15, 2020): 10743–54. http://dx.doi.org/10.1175/jcli-d-20-0542.1.
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AbstractBased on 36-yr hindcasts from the fifth-generation seasonal forecast system of the European Centre for Medium-Range Weather Forecasts (SEAS5), the most predictable patterns of the wintertime 2-m air temperature (T2m) in the extratropical Northern Hemisphere are extracted via the maximum signal-to-noise (MSN) empirical orthogonal function (EOF) analysis, and their associated predictability sources are identified. The MSN EOF1 captures the warming trend that amplifies over the Arctic but misses the associated warm Arctic–cold continent pattern. The MSN EOF2 delineates a wavelike T2m pattern over the Pacific–North America region, which is rooted in the tropical forcing of the eastern Pacific-type El Niño–Southern Oscillation (ENSO). The MSN EOF3 shows a wavelike T2m pattern over the Pacific–North America region, which has an approximately 90° phase difference from that associated with MSN EOF2, and a loading center over midlatitude Eurasia. Its sources of predictability include the central Pacific-type ENSO and Eurasian snow cover. The MSN EOF4 reflects T2m variability surrounding the Tibetan Plateau, which is plausibly linked to the remote forcing of the Arctic sea ice. The information on the leading predictable patterns and their sources of predictability is further used to develop a calibration scheme to improve the prediction skill of T2m. The calibrated prediction skill in terms of the anomaly correlation coefficient improves significantly over midlatitude Eurasia in a leave-one-out cross-validation, implying a possible way to improve the wintertime T2m prediction in the SEAS5.
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Aprillia, Happy, Hong-Tzer Yang, and Chao-Ming Huang. "Short-Term Photovoltaic Power Forecasting Using a Convolutional Neural Network–Salp Swarm Algorithm." Energies 13, no. 8 (April 12, 2020): 1879. http://dx.doi.org/10.3390/en13081879.
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The high utilization of renewable energy to manage climate change and provide green energy requires short-term photovoltaic (PV) power forecasting. In this paper, a novel forecasting strategy that combines a convolutional neural network (CNN) and a salp swarm algorithm (SSA) is proposed to forecast PV power output. First, the historical PV power data and associated weather information are classified into five weather types, such as rainy, heavy cloudy, cloudy, light cloudy and sunny. The CNN classification is then used to determine the prediction for the next day’s weather type. Five models of CNN regression are established to accommodate the prediction for different weather types. Each CNN regression is optimized using a salp swarm algorithm (SSA) to tune the best parameter. To evaluate the performance of the proposed method, comparisons were made to the SSA based support vector machine (SVM-SSA) and long short-term memory neural network (LSTM-SSA) methods. The proposed method was tested on a PV power generation system with a 500 kWp capacity located in south Taiwan. The results showed that the proposed CNN-SSA could accommodate the actual generation pattern better than the SVM-SSA and LSTM-SSA methods.
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Ghutake, Ishita, Ritesh Verma, Rohit Chaudhari, and Vidhate Amarsinh. "An intelligent Crop Price Prediction using suitable Machine Learning Algorithm." ITM Web of Conferences 40 (2021): 03040. http://dx.doi.org/10.1051/itmconf/20214003040.
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Planning of crops for the next season has been a tedious task for the farmers as it is a difficult prediction about metrics of prices that their crop will fetch in a particular season which will be typically based on dynamic weather conditions. This leads to inaccurate prediction of crops’ prices by farmers, and they happen to wrongly select the crops or in haste they happen to sell their crops early without storing and thus earning less than what the same crop would have fetched them in the future. This problem could be addressed by an ML model which will predict the prices of crops in advance showing the proper analysis of the crop and presenting their future scenario so that farmers can select the right crops to strategize crop production which involves crop selection, time of sowing deciding crop pattern and storage of harvested crops providing enough insights for predicting the appropriate price in the markets.
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Kim, Kyosik, Byunghyun Kim, and Kun-Yeun Han. "Performance Evaluation of Effective Drought Prediction Using Machine Learning." Journal of the Korean Society of Hazard Mitigation 21, no. 2 (April 30, 2021): 195–204. http://dx.doi.org/10.9798/kosham.2021.21.2.195.
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There has been much research recently to improve the prediction of drought, but the frequency and pattern of drought displays an irregular time series that limits its predictability, making it difficult to predict with only a single model, and high-level predictions cannot be made even when many models are applied. Therefore, many studies have been conducted to improve predictions by using explanatory variables such as precipitation, temperature, sunshine duration, and air volume as input data. The purpose of this study is to devise a method for predicting drought using the Standard Precipitation Evaporation Index (SPEI), which represents a complex and difficult time series drought index using climate data for weather phenomena. The Standard Precipitation Evaporation Index is a method of calculating the cumulative precipitation by excluding the cumulative evaporation amount from the cumulative precipitation using precipitation and evapotranspiration data, and the evaporation amount is calculated using the monthly heat index method. The Meteorological Agency evaluated meteorological drought using SPI6, which is a 6-month cumulative precipitation standard, and applied it to machine learning based on monthly data and daily data SPEI6 in this study. As a result, ANN monthly data R2 was 0.488 in Andong and 0.533 in Mungyeong, Gumi 0.594, SVR 0.452, 0.496, 0.564, RF 0.355, 0.467, 0.524, and the daily data are ANN 0.923, 0.919, 0.915, SVR 0.925, 0.923, 0.896, RF 0.915, 0.915, 0.797, and the daily data SPEI at all points. It was confirmed that high prediction was obtained when machine learning was applied to these methods.
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Lin, Hsin-Hung, Chih-Chien Tsai, Jia-Chyi Liou, Yu-Chun Chen, Chung-Yi Lin, Lee-Yaw Lin, and Kao-Shen Chung. "Multi-Weather Evaluation of Nowcasting Methods Including a New Empirical Blending Scheme." Atmosphere 11, no. 11 (October 29, 2020): 1166. http://dx.doi.org/10.3390/atmos11111166.
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This study utilized a radar echo extrapolation system, a high-resolution numerical model with radar data assimilation, and three blending schemes including a new empirical one, called the extrapolation adjusted by model prediction (ExAMP), to carry out 150 min reflectivity nowcasting experiments for various heavy rainfall events in Taiwan in 2019. ExAMP features full trust in the pattern of the extrapolated reflectivity with intensity adjustable by numerical model prediction. The spatial performance for two contrasting events shows that the ExAMP scheme outperforms the others for the more accurate prediction of both strengthening and weakening processes. The statistical skill for all the sampled events shows that the nowcasts by ExAMP and the extrapolation system obtain the lowest and second lowest root mean square errors at all the lead time, respectively. In terms of threat scores and bias scores above certain reflectivity thresholds, the ExAMP nowcast may have more grid points of misses for high reflectivity in comparison to extrapolation, but serious overestimation among the points of hits and false alarms is the least likely to happen with the new scheme. Moreover, the event type does not change the performance ranking of the five methods, all of which have the highest predictability for a typhoon event and the lowest for local thunderstorm events.
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Patnaik, Naresh, and F. Baliarsingh. "Weather Forecasting in Coastal Districts of Odisha and Andhra Pradesh by Using Time Series Analysis." International Journal of Emerging Research in Management and Technology 6, no. 8 (June 25, 2018): 85. http://dx.doi.org/10.23956/ijermt.v6i8.122.
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Climate change in world is always one of the most important topics in Water Resources. Now the issue is so predominant that it is gradually restricting out social life, peace and harmony. Climate change is a change in the statistical distribution of weather pattern of an area, when such changes occur for a long period of time. Weather is the state of atmosphere at a particular place and time. Climate is the long term statistical expression of short term weather. This study presents a comprehensive assessment of the future climate pattern/weather prediction by taking different climatic parameters such as temperature, precipitation, solar radiation, wind speed and relative humidity by using time series analysis. The study area of research work covers the coastal districts of Odisha and some parts of Andhra Pradesh. The climatic parameters are collected over last 20 years (1993-2013) from the selected 10 stations and the prediction is made using Time Series Analysis (ARIMA Model). The annual maximum temperature, solar radiation of all districts indicates a statistically significant increase in trend, whereas in the case of wind speed and relative humidity indicates significant deceasing trend. The annual rain fall shows an increasing trend of 2.69 mm/year in all station except Srikakulam, Khordha, Jagatsinghpur and Balasore which shows a decreasing trend of 1.94, 1.29, 0.56 and 1.18 mm/year respectively. As a whole the annual maximum temperature and solar radiation shows an increase trend of 0.16 ⁰C and 0.073 MJ/m² per year respectively. Further the wind speed and relative humidity of all stations indicates a decreasing trend of 0.056 m/s and 0.003(Units in fraction) per year respectively.
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Sengupta, Saheli, Aritra Ghosh, Tapas K. Mallick, Chandan Kumar Chanda, Hiranmay Saha, Indrajit Bose, Joydip Jana, and Samarjit Sengupta. "Model Based Generation Prediction of SPV Power Plant Due to Weather Stressed Soiling." Energies 14, no. 17 (August 26, 2021): 5305. http://dx.doi.org/10.3390/en14175305.
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Solar energy is going to be a major component of global energy generation. Loss due to dust deposition has raised a great concern to the investors in this field. Pre-estimation of this reduced generation and hence the economic loss will help the operators’ readiness for efficient and enhanced economic energy management of the system. In an earlier article, a physics–based model is proposed for assessment of dust accumulation under various climatic conditions which is validated by data of a single location. In this paper, the universality of this model is established and is used to demonstrate the effect of generation loss due to dust deposition and of cleaning. Variation in the soiling pattern due to climatic covariates has also been studied. Generation loss is calculated for Solar Photovoltaic power plants of different capacities at various locations in India. Finally this model has also been extended to predict the generation accounting for the soiling loss in Photovoltaic system. All the calculated and predicted results are validated with the measured values of the above plants.
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Allen, Michael J., Thomas R. Allen, Christopher Davis, and George McLeod. "Exploring Spatial Patterns of Virginia Tornadoes Using Kernel Density and Space-Time Cube Analysis (1960–2019)." ISPRS International Journal of Geo-Information 10, no. 5 (May 7, 2021): 310. http://dx.doi.org/10.3390/ijgi10050310.
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This study evaluates the spatial-temporal patterns in Virginia tornadoes using the National Weather Service Storm Prediction Center’s Severe Weather GIS (SVRGIS) database. In addition to descriptive statistics, the analysis employs Kernel Density Estimation for spatial pattern analysis and space-time cubes to visualize the spatiotemporal frequency of tornadoes and potential trends. Most of the 726 tornadoes between 1960–2019 occurred in Eastern Virginia, along the Piedmont and Coastal Plain. Consistent with other literature, both the number of tornadoes and the tornado days have increased in Virginia. While 80% of the tornadoes occurred during the warm season, tornadoes did occur during each month including two deadly tornadoes in January and February. Over the 60-year period, a total of 28 people were killed in the Commonwealth. Most tornado activity took place in the afternoon and early evening hours drawing attention to the temporal variability of risk and vulnerability. Spatial analysis results identify significant, non-random clusters of tornado activity and increasing temporal frequency. While this study improves weather-related literacy and addresses a need in the Commonwealth, more research is necessary to further evaluate the synoptic and mesoscale mechanisms of Virginia tornadoes.
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Basu, Soumik, and David Sauchyn. "An Unusual Cold February 2019 in Saskatchewan—A Case Study Using NCEP Reanalysis Datasets." Climate 7, no. 7 (July 3, 2019): 87. http://dx.doi.org/10.3390/cli7070087.
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In February 2019, central Canada, and especially the province of Saskatchewan, experienced extreme cold weather. It was the coldest February in 82 years and the second coldest in 115 years. In this study, we examine National Centers for Environmental Prediction (NCEP)/National Center for Atmospheric Research (NCAR) Reanalysis 1 data to understand the atmospheric processes leading to this cold snap. A detailed investigation of surface air temperature, sea level pressure, surface fluxes, and winds revealed a linkage between the North Pacific storm track and the February cold snap. A shift in the jet stream pattern triggered by the storm activity over the North Pacific caused a high-pressure blocking pattern, which resulted in unusual cold temperatures in Saskatchewan in February. This study demonstrates the potential for extreme cold in a warming climate; weather records in Saskatchewan show an increase in minimum winter temperature by 4–5 °C.
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AghaKouchak, Amir, Nasrin Nasrollahi, Jingjing Li, Bisher Imam, and Soroosh Sorooshian. "Geometrical Characterization of Precipitation Patterns." Journal of Hydrometeorology 12, no. 2 (April 1, 2011): 274–85. http://dx.doi.org/10.1175/2010jhm1298.1.
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Abstract Satellite estimates and weather forecast models have made it possible to observe and predict precipitation over large spatial scales. Despite substantial progress in observing patterns of precipitation, characterization of spatial patterns is still a challenge. Quantitative assessment methods for spatial patterns are essential for future developments in prediction of the spatial extent and patterns of precipitation. In this study, precipitation patterns are characterized using three geometrical indices: (i) a connectivity index, (ii) a shape index, and (iii) a dispersiveness index. Using multiple examples, the application of the proposed indices is explored in pattern analysis of satellite precipitation images and validation of numerical atmospheric models with respect to geometrical properties. The results indicate that the presented indices can be reasonably employed for a relative comparison of different patterns (e.g., multiple fields against spatial observations) with respect to their connectivity, organization, and shape.
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Duell, Rebecca S., and Matthew S. Van Den Broeke. "Climatology, Synoptic Conditions, and Misanalyses of Mississippi River Valley Drylines." Monthly Weather Review 144, no. 3 (February 16, 2016): 927–43. http://dx.doi.org/10.1175/mwr-d-15-0108.1.
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Abstract The dryline is an important focal point for convection initiation. Although drylines most commonly occur on the southern Great Plains, dryline passages and subsequent severe weather outbreaks have been documented in the Mississippi River valley. This study presents a 15-yr (1999–2013) climatology of these Mississippi River valley drylines and associated severe weather. Additionally, synoptic patterns are identified that may result in drylines moving atypically far eastward into the Mississippi River valley. In total, 39 Mississippi River valley drylines (hereafter referred to as MRV drylines) were identified from the North American Regional Reanalysis (NARR) dataset through the study period. Mean and anomaly synoptic composites were created for these drylines. MRV dryline events typically occur under synoptically active conditions with an amplified upper-air pattern, a 500-hPa shortwave trough to the west or northwest of the dryline, and a strong surface cyclone to the north. These boundaries are often misanalyzed or inconsistently analyzed as cold fronts, stationary fronts, or trough axes on surface maps; of the 33 cases of identified MRV drylines for which the Weather Prediction Center archived analyses were available, only 6 were correctly analyzed as drylines. Drylines moving into the Mississippi River valley often result in severe weather outbreaks in the Mississippi River valley, the Midwest, and the southeastern United States.
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Jiang, Xiaowei, Jun Li, Zhenglong Li, Yunheng Xue, Di Di, Pei Wang, and Jinlong Li. "Evaluation of Environmental Moisture from NWP Models with Measurements from Advanced Geostationary Satellite Imager—A Case Study." Remote Sensing 12, no. 4 (February 18, 2020): 670. http://dx.doi.org/10.3390/rs12040670.
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The distribution of tropospheric moisture in the environment is highly associated with storm development. Therefore, it is important to evaluate the uncertainty of moisture fields from numerical weather prediction (NWP) models for better understanding and enhancing storm prediction. With water vapor absorption band radiance measurements from the advanced imagers onboard the new generation of geostationary weather satellites, it is possible to quantitatively evaluate the environmental moisture fields from NWP models. Three NWP models—Global Forecast System (GFS), Unified Model (UM), Weather Research and Forecasting (WRF)—are evaluated with brightness temperature (BT) measurements from the three moisture channels of Advanced Himawari Imager (AHI) onboard the Himawari-8 satellite for Typhoon Linfa (2015) case. It is found that the three NWP models have similar performance for lower tropospheric moisture, and GFS has a smaller bias for middle tropospheric moisture. Besides, there is a close relationship between moisture forecasts in the environment and the tropical cyclone (TC) track forecasts in GFS, while regional WRF does not show this pattern. When the infrared and microwave sounder radiance measurements from polar orbit satellite are assimilated in regional WRF, it is clearly shown that the environment moisture fields are improved compared with that with only conventional data are assimilated.
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Wang, Yizhen, Ningqing Zhang, and Xiong Chen. "A Short-Term Residential Load Forecasting Model Based on LSTM Recurrent Neural Network Considering Weather Features." Energies 14, no. 10 (May 11, 2021): 2737. http://dx.doi.org/10.3390/en14102737.
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With economic growth, the demand for power systems is increasingly large. Short-term load forecasting (STLF) becomes an indispensable factor to enhance the application of a smart grid (SG). Other than forecasting aggregated residential loads in a large scale, it is still an urgent problem to improve the accuracy of power load forecasting for individual energy users due to high volatility and uncertainty. However, as an important variable that affects the power consumption pattern, the influence of weather factors on residential load prediction is rarely studied. In this paper, we review the related research of power load forecasting and introduce a short-term residential load forecasting model based on a long short-term memory (LSTM) recurrent neural network with weather features as an input.
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Tajbakhsh, S., P. Ghafarian, and F. Sahraian. "Instability indices and forecasting thunderstorms: the case of 30 April 2009." Natural Hazards and Earth System Sciences 12, no. 2 (February 17, 2012): 403–13. http://dx.doi.org/10.5194/nhess-12-403-2012.
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Abstract. In this paper, one meteorological case study for two Iranian airports are presented. Attempts have been made to study the predefined threshold amounts of some instability indices such as vertical velocity and relative humidity. Two important output variables from a numerical weather prediction model have been used to survey thunderstorms. The climatological state of thunder days in Iran has been determined to aid in choosing the airports for the case studies. The synoptic pattern, atmospheric thermodynamics and output from a numerical weather prediction model have been studied to evaluate the occurrence of storms and to verify the threshold instability indices that are based on Gordon and Albert (2000) and Miller (1972). Using data from the Statistics and Data Center of the Iran Meteorological Organization, 195 synoptic stations were used to study the climatological pattern of thunderstorm days in Iran during a 15-yr period (1991–2005). Synoptic weather maps and thermodynamic diagrams have been drawn using data from synoptic stations and radiosonde data. A 15-km resolution version of the WRF numerical model has been implemented for the Middle East region with the assistance of global data from University Corporation for Atmospheric Research (UCAR). The Tabriz airport weather station has been selected for further study due to its high frequency of thunderstorms (more than 35 thunderstorm days per year) and the existence of an upper air station. Despite the fact that storms occur less often at the Tehran weather station, the station has been chosen as the second case study site due to its large amount of air traffic. Using these two case studies (Tehran at 00:00 UTC, 31 April 2009 and Tabriz at 12:00 UTC, 31 April 2009), the results of this research show that the threshold amounts of 30 °C for KI, −2 °C for LI and −3 °C for SI suggests the occurrence and non-occurrence of thunderstorms at the Tehran and Tabriz stations, respectively. The WRF model output of vertical velocity and relative humidity are the two most important indices for examining storm occurrence, and they have a numerical threshold of 1 m s−1 and 80%, respectively. These results are comparable to other studies that have examined thunderstorm occurrence.
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Wirahma, Samba, Ibnu Athoillah, and Sutrisno . "PERBANDINGAN PREDIKSI CURAH HUJAN GFS METEOROGRAM DENGAN CURAH HUJAN TRMM DI DAS RIAM KANAN KALIMANTAN SELATAN." Jurnal Sains & Teknologi Modifikasi Cuaca 16, no. 2 (December 2, 2015): 73. http://dx.doi.org/10.29122/jstmc.v16i2.1049.
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Teknologi Modifikasi Cuaca (TMC) yang diterapkan oleh BPPT di Kalimantan Selatan dilakukan guna mengatasi kekurangan debit air yang terjadi pada DAS Riam Kanan. Untuk melaksanakan TMC yang efektif dan efisien dibutuhkan prediksi cuaca harian yang akurat dan mendetail pada catchment area (daerah tangkapan hujan) tersebut, khususnya prediksi curah hujan harian. TMC yang diterapkan oleh BPPT menggunakan prediksi yang salah satunya diambil dari Global Forecast System (GFS) Meteorogram. Prediksi tersebut bisa menjadi referensi untuk mengolah dan menganalisis parameter cuaca dengan baik, serta merencanakan dan memutuskan pelaksanaan penerbangan eksekusi selama kegiatan TMC. Untuk menguji ketepatan suatu prediksi, maka diperlukan validasi/perbandingan hasil prediksi dengan data real, yaitu data curah hujan yang dapat diambil dari data Tropical Rainfall Measuring Mission (TRMM).Prediksi curah hujan menggunakan GFS Meteorogram dibandingkan dengan data curah hujan dari TRMM di daerah DAS Riam Kanan menggunakan korelasi Pearson, pengambilan data prediksi GFS dilakukan mulai dari 16 Mei 2014 s/d 31 Mei 2014. Koefisien korelasi yang diambil hanya yang memiliki pola/bentuk hubungan korelasi linear positif (+1). Dari hasil analisis korelasi didapatkan bahwa dari 16 hari pengambilan data di semua lokasi, rata-rata terdapat 8 - 11 hari yang memiliki nilai koefisien korelasi (KK) positif untuk prediksi di hari yang sama dan 6 - 11 hari untuk prediksi Lag_1, dengan nilai KK yang paling banyak muncul yaitu : range 0.4 - 0.7 untuk prediksi 7 hari ke depan, range 0.7 - 0.9 untuk prediksi 5 hari ke depan, dan range 0.9 - 1 untuk prediksi 3 hari ke depan. Dari keenam lokasi titik prediksi dengan nilai koefisien korelasi linear positif yang paling banyak muncul dan memiliki hubungan yang paling kuat adalah di titik Banjarmasin dan DAS bagian Utara.Kata Kunci : prediksi curah hujan GFS, curah hujan TRMM DAS Riam Kanan, koefisien korelasiWeather Modification Technology applied by BPPT in South Kalimantan in order to overcome the shortage of water discharge that occurs in the Riam Kanan Watershed. To implement the weather modification technology an effective and efficient required daily weather predictions are accurate and detail in the catchment area, especially dailiy rainfall prediction. In this Technology, BPPT using prediction from the Global Forecast System (GFS) Meteorogram. This prediction could be a reference to analyze weather parameter, planning, and deciding to do flight execution for weather modification. To verifying accuracy of this prediction, it is necessary validation/ comparison with real data that can be retrieved from the data Tropical Rainfall Measuring Mission (TRMM).Rainfall prediction of GFS Meteorogram compared with data from TRMM rainfall on the Riam Kanan Watershed using Pearson Correlation, GFS forecast data collected from May 16 - May 3,1 2014. The correlation coefficient is taken only has a pattern a positive linear correlation. The result from correlation analysis showed that 16 days of data collection in all locations, on average there are 8 – 11 days have a correlation coefficient is positive for prediction in the same day, and 6 – 11 days for prediction in lag_1 with most value arise of correlation coefficient is 0.4 – 0.7 for prediction of next 7 days, range 0.7 – 0.9 for prediction of next 5 days, and range 0.9 – 1 for prediction of next 3 days. From the six location of prediction points with most value arise of correlation coefficient positive linier and have the strongest relation are in Banjarmasin and northern watershed.Keywords : GFS precipitation forecast, Riam Kanan TRMM rainfall, correlation coefficient
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Wang, Yan, Hong-Li Ren, Fang Zhou, Joshua-Xiouhua Fu, Quan-Liang Chen, Jie Wu, Wei-Hua Jie, and Pei-Qun Zhang. "Multi-Model Ensemble Sub-Seasonal Forecasting of Precipitation over the Maritime Continent in Boreal Summer." Atmosphere 11, no. 5 (May 16, 2020): 515. http://dx.doi.org/10.3390/atmos11050515.
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The Maritime Continent (MC) is a critical region with unique geographical conditions and significant monsoon activities that plays a vital role in global climate variation. In this study, the weekly prediction of precipitation over the MC during boreal summer (from May to September) was analyzed using the 12-year reforecasts data from five Sub-seasonal to Seasonal (S2S) models, including the China Meteorological Administration (CMA), the European Centre for Medium-Range Weather Forecasts (ECMWF), Environment and Climate Change Canada (ECCC), the National Centers for Environmental Prediction (NCEP), and the Met Office (UKMO). The result shows that, compared with the individual models, our newly derived median multi-model ensemble (MME) can significantly improve the prediction skill of sub-seasonal precipitation in the MC. Both the Temporal Correlation Coefficient (TCC) skill and the Pattern Correlation Coefficient (PCC) skill reached 0.6 in lead week 1, dropped the following week, did not exceed 0.2 in lead week 3, and then lost their significance. The results show higher prediction skill near the Equator than in the north at 10° N. It is difficult to make effective predictions with the models beyond three weeks. The prediction ability of the median MME improves significantly as the total number of model members increases. The prediction performance of the median MME depends not only on the diversity of models but also on the number of model members. Moreover, the prediction skill is particularly sensitive to the intensity and phase of Boreal Summer Intraseasonal Oscillation 1 (BSISO1) with the highest skills appearing at initial phases 1 and 5.
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Tseng, Kai-Chih, Eric Maloney, and Elizabeth Barnes. "The Consistency of MJO Teleconnection Patterns: An Explanation Using Linear Rossby Wave Theory." Journal of Climate 32, no. 2 (December 28, 2018): 531–48. http://dx.doi.org/10.1175/jcli-d-18-0211.1.
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Abstract The Madden–Julian oscillation (MJO) excites strong variations in extratropical atmospheric circulations that have important implications for subseasonal-to-seasonal (S2S) prediction. A previous study showed that particular MJO phases are characterized by a consistent modulation of geopotential heights in the North Pacific and adjacent regions across different MJO events, and demonstrated that this consistency is beneficial for extended numerical weather forecasts (i.e., lead times of two weeks to one month). In this study, we examine the physical mechanisms that lead some MJO phases to have more consistent teleconnections than others using a linear baroclinic model. The results show that MJO phases 2, 3, 6, and 7 consistently generate Pacific–North American (PNA)-like patterns on S2S time scales while other phases do not. A Rossby wave source analysis is applied and shows that a dipole-like pattern of Rossby wave source on each side of the subtropical jet can increase the pattern consistency of teleconnections due to the constructive interference of similar teleconnection signals. On the other hand, symmetric patterns of Rossby wave source can dramatically reduce the pattern consistency due to destructive interference. A dipole-like Rossby wave source pattern is present most frequently when tropical heating is found in the Indian Ocean or the Pacific warm pool, and a symmetric Rossby wave source is present most frequently when tropical heating is located over the Maritime Continent. Thus, the MJO phase-dependent pattern consistency of teleconnections is a special case of this mechanism.
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Li, Youru, Zhenfeng Zhu, Deqiang Kong, Meixiang Xu, and Yao Zhao. "Learning Heterogeneous Spatial-Temporal Representation for Bike-Sharing Demand Prediction." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 1004–11. http://dx.doi.org/10.1609/aaai.v33i01.33011004.
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Bike-sharing systems, aiming at meeting the public’s need for ”last mile” transportation, are becoming popular in recent years. With an accurate demand prediction model, shared bikes, though with a limited amount, can be effectively utilized whenever and wherever there are travel demands. Despite that some deep learning methods, especially long shortterm memory neural networks (LSTMs), can improve the performance of traditional demand prediction methods only based on temporal representation, such improvement is limited due to a lack of mining complex spatial-temporal relations. To address this issue, we proposed a novel model named STG2Vec to learn the representation from heterogeneous spatial-temporal graph. Specifically, we developed an event-flow serializing method to encode the evolution of dynamic heterogeneous graph into a special language pattern such as word sequence in a corpus. Furthermore, a dynamic attention-based graph embedding model is introduced to obtain an importance-awareness vectorized representation of the event flow. Additionally, together with other multi-source information such as geographical position, historical transition patterns and weather, e.g., the representation learned by STG2Vec can be fed into the LSTMs for temporal modeling. Experimental results from Citi-Bike electronic usage records dataset in New York City have illustrated that the proposed model can achieve competitive prediction performance compared with its variants and other baseline models.
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Mei, Bin, Licheng Sun, and Guoyou Shi. "Full-Scale Maneuvering Trials Correction and Motion Modelling Based on Actual Sea and Weather Conditions." Sensors 20, no. 14 (July 16, 2020): 3963. http://dx.doi.org/10.3390/s20143963.
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Aiming at the poor accuracy and difficult verification of maneuver modeling induced by the wind, waves and sea surface currents in the actual sea, a novel sea trials correction method for ship maneuvering is proposed. The wind and wave drift forces are calculated according to the measurement data. Based on the steady turning hypothesis and pattern search algorithm, the adjustment parameters of wind, wave and sea surface currents were solved, the drift distances and drift velocities of wind, waves and sea surface currents were calculated and the track and velocity data of the experiment were corrected. The hydrodynamic coefficients were identified by the test data and the ship maneuvering motion model was established. The results show that the corrected data were more accurate than log data, the hydrodynamic coefficients can be completely identified, the prediction accuracy of the advance and tactical diameters were 93% and 97% and the prediction of the maneuvering model was accurate. Numerical cases verify the correction method and full-scale maneuvering model. The turning circle advance and tactical diameter satisfy the standards of the ship maneuverability of International Maritime Organization (IMO).
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Samy, V. Sakthivel, Koyel Pramanick, Veena Thenkanidiyoor, and Jeni Victor. "Data Analysis and Visualization in Python for Polar Meteorological Data." International Journal of Data Analytics 2, no. 1 (January 2021): 32–60. http://dx.doi.org/10.4018/ijda.2021010102.
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The aim of this study is to analyze meteorological data obtained from the various expeditions made to the Indian stations in Antarctica over recent years and determine how significantly the weather has shown a marked change over the years. For any time series data analysis, there are two main goals: (a) the authors need to identify the nature of the phenomenon from the sequence of observations and (b) predict the future data. On account of these goals, the pattern in the time series data and its variability are to be accurately identified. This paper can then interpret and integrate the pattern established with its associated meteorological datasets collected in Antarctica. Using the data analytics knowledge the validity of interpretation for the given datasets a pattern has been identified, which could extrapolate the pattern towards prediction. To ease the time series data analysis, the authors developed online meteorological data analytic portal at NCPOR, Goa http://data.ncaor.gov.in/.
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Hou, Jie, Ping Wang, and Shuo Zhuang. "A New Method of Characterizing Flow Patterns of Vortices and Detecting the Centers of Vortices in a Numerical Wind Field." Journal of Atmospheric and Oceanic Technology 34, no. 1 (January 2017): 101–15. http://dx.doi.org/10.1175/jtech-d-15-0197.1.
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AbstractA vortex in a wind field is an important aspect of a weather system; vortices often result in hazardous weather, such as rainstorms, windstorms, and typhoons. As the availability of numerical meteorological data increases, traditional manual analysis no longer provides an efficient means of timely analysis of observed and predicted atmospheric vortices. Therefore, a method was proposed to automatically characterize flow patterns of vortices and to detect the centers of vortices in complex wind fields generated from numerical weather prediction (NWP) models. First, a statistical feature was developed to preliminarily filter regional wind data to obtain (anti)cyclonic vortices. Second, flow patterns of ideal axisymmetric wind fields were extracted by analyzing circular data related to wind directions. Third, for actual vortices in a complex wind field, a series of rules and deformation degree indices were constructed to retrieve the provisional centers of vortices. Fourth, the Ward hierarchical clustering algorithm was used to cluster these provisional centers, which were filled up by a dilation operation to cover the core region of the vortex. Finally, the vortices were classified as either cyclones or anticyclones based on their analyzed vorticity, and their global centers were precisely located. Experimental results show that the proposed preprocessing method was more effective than the traditional filtering method and that the features of the flow pattern were stable regardless of the variety in the resolution and scale. It was also proven that the proposed method can be further extended and applied to detecting typhoon centers, for which it was more effective than other currently used methods.
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Ivanov, Serguei, Silas Michaelides, and Igor Ruban. "Mesoscale Resolution Radar Data Assimilation Experiments with the Harmonie Model." Remote Sensing 10, no. 9 (September 11, 2018): 1453. http://dx.doi.org/10.3390/rs10091453.
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This study presents a pre-processing approach adopted for the radar reflectivity data assimilation and results of simulations with the Harmonie numerical weather prediction model. The proposed method creates a 3D regular grid in which a horizontal size of meshes coincides with the horizontal model resolution. This minimizes the representative error associated with the discrepancy between resolutions of informational sources. After such preprocessing, horizontal structure functions and their gradients for radar reflectivity maintain the sizes and shapes of precipitation patterns similar to those of the original data. The method shows an improvement of precipitation prediction within the radar location area in both the rain rates and spatial pattern presentation. It redistributes precipitable water with smoothed values over the common domain since the control runs show, among several sub-domains with increased and decreased values, correspondingly. It also reproduces the mesoscale belts and cell patterns of sizes from a few to ten kilometers in precipitation fields. With the assimilation of radar data, the model simulates larger water content in the middle troposphere within the layer from 1 km to 6 km with major variations at 2.5 km to 3 km. It also reproduces the mesoscale belt and cell patterns of precipitation fields.
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Bierkens, M. F. P., and L. P. H. van Beek. "Seasonal Predictability of European Discharge: NAO and Hydrological Response Time." Journal of Hydrometeorology 10, no. 4 (August 1, 2009): 953–68. http://dx.doi.org/10.1175/2009jhm1034.1.
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Abstract In this paper the skill of seasonal prediction of river discharge and how this skill varies between the branches of European rivers across Europe is assessed. A prediction system of seasonal (winter and summer) discharge is evaluated using 1) predictions of the average North Atlantic Oscillation (NAO) index for the coming winter based on May SST anomalies of the North Atlantic; 2) a global-scale hydrological model; and 3) 40-yr European Centre for Medium-Range Weather Forecasts Re-Analysis (ERA-40) data. The skill of seasonal discharge predictions is investigated with a numerical experiment. Also Europe-wide patterns of predictive skill are related to the use of NAO-based seasonal weather prediction, the hydrological properties of the river basin, and a correct assessment of initial hydrological states. These patterns, which are also corroborated by observations, show that in many parts of Europe the skill of predicting winter discharge can, in theory, be quite large. However, this achieved skill mainly comes from knowing the correct initial conditions of the hydrological system (i.e., groundwater, surface water, soil water storage of the basin) rather than from the use of NAO-based seasonal weather prediction. These factors are equally important for predicting subsequent summer discharge.
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Wang, Yuanbing, Yaodeng Chen, and Jinzhong Min. "Impact of Assimilating China Precipitation Analysis Data Merging with Remote Sensing Products Using the 4DVar Method on the Prediction of Heavy Rainfall." Remote Sensing 11, no. 8 (April 23, 2019): 973. http://dx.doi.org/10.3390/rs11080973.
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In this study, the China Hourly Merged Precipitation Analysis (CHMPA) data which combines the satellite-retrieved Climate Prediction Center Morphing (CMORPH) with the automatic weather station precipitation observations is firstly assimilated into the Weather Research and Forecasting (WRF) model using the Four-Dimensional Variational (4DVar) method. The analyses and subsequent forecasts of heavy rainfall during Meiyu season occurred in July 2013 over eastern China is evaluated. Besides, the sensitivity of rainfall forecast skill of assimilating the CHMPA data to the rainfall error, the rainfall thinning distance, and the rainfall accumulation time within assimilation window are investigated in this study. Then, the impact of 4DVar data assimilation with and without CHMPA rainfall data is evaluated to show how the assimilation of CHMPA impacts the precipitation simulations. It is found that assimilation of the CHMPA data helps to produce a better short-range precipitation forecast in this study. The rainfall fields after assimilation of CHMPA is closer to observations in terms of quantity and pattern. However, the leading time of improved forecast is limited to about 18 hours. It is also found that CHMPA data assimilation produces stronger realistic moisture divergence, precipitabale water field and the vertical wind field in the forecasting fields, which eventually contributes to the improved forecast of heavy rainfall. This study can provide references for the assimilation of CHMPA data into the WRF model using 4DVar, which is valuable for limited-area numerical weather prediction and hydrological applications.