A novel weather parameters prediction scheme and their effects on crops

Weather forecast is significantly imperative in today’s smart technological world. A precise forecast model entails a plentiful data in order to attain the most accurate predictions. However, a forecast of future rainfall from historical data samples has always been challenging and key area of research. Hence, in modern weather forecasting a combo of computer models, observation, and knowledge of trends and patterns are introduced. This research work has presented a fitness function based adaptive artificial neural network scheme in order to forecast rainfall and temperature for upcoming decade (2021-2030) using historical weather data of 20 different districts of Karnataka state. Furthermore, effects of these forecasted weather parameters are realized over five major crops of Karnataka namely rice, wheat, jowar, maize, and ragi with the intention of evaluation for efficient crop management in terms of the passing relevant messages to the farmers and alternate measures such as suggesting other geographical locations to grow the same crop or growing other suitable crops at same geographical location. A graphical user interface (GUI) application has been developed for the proposed work in order to ease out the flow of work.

Wind, Solar, and Photovoltaic Renewable Energy Systems with and without Energy Storage Optimization: A Survey of Advanced Machine Learning and Deep Learning Techniques

Nowadays, learning-based modeling methods are utilized to build a precise forecast model for renewable power sources. Computational Intelligence (CI) techniques have been recognized as effective methods in generating and optimizing renewable tools. The complexity of this variety of energy depends on its coverage of large sizes of data and parameters, which have to be investigated thoroughly. This paper covered the most resent and important researchers in the domain of renewable problems using the learning-based methods. Various types of Deep Learning (DL) and Machine Learning (ML) algorithms employed in Solar and Wind energy supplies are given. The performance of the given methods in the literature is assessed by a new taxonomy. This paper focus on conducting comprehensive state-of-the-art methods heading to performance evaluation of the given techniques and discusses vital difficulties and possibilities for extensive research. Based on the results, variations in efficiency, robustness, accuracy values, and generalization capability are the most obvious difficulties for using the learning techniques. In the case of the big dataset, the effectiveness of the learning techniques is significantly better than the other computational methods. However, applying and producing hybrid learning techniques with other optimization methods to develop and optimize the construction of the techniques is optionally indicated. In all cases, hybrid learning methods have better achievement than a single method due to the fact that hybrid methods gain the benefit of two or more techniques for providing an accurate forecast. Therefore, it is suggested to utilize hybrid learning techniques in the future to deal with energy generation problems.

Efficiency of earthquake forecast models based on earth tidal correlation with background seismicity along the Tonga–Kermadec trench

The correlation between Earth’s tides and background seismicity has been suggested to become stronger before great earthquakes and weaker after. However, previous studies have only retrospectively analyzed this correlation after individual large earthquakes; it thus remains vague (i) whether such variations might be expected preceding future large earthquakes, and (ii) the strength of the tidal correlation during interseismic periods. Therefore, we retrospectively investigated whether significant temporal variations of the tidal correlation precede large interplate earthquakes along the Tonga–Kermadec trench, where Mw 7-class earthquakes frequently occurred from 1977 to 31 December 2020. We evaluated a forecast model based on the temporal variations of the tidal correlation via Molchan’s error diagram, using the tidal correlation value itself as well as its rate of change as threshold values. For Mw ≥ 7.0 earthquakes, this model was as ineffective as random guessing. For Mw ≥ 6.5, 6.0, or 5.5 earthquakes, the forecast model performed better than random guessing in some cases, but even the best forecast only had a probability gain of about 1.7. Therefore, the practicality of this model alone is poor, at least in this region. These results suggest that changes of the tidal correlation are not reliable indicators of large earthquakes along the Tonga–Kermadec trench. Graphical Abstract

A Dynamic Statistical Subseasonal Forecast Model for OLR Over Tropical Pacific Region

How to improve the subseasonal forecast skills of dynamic models has always been an important issue in atmospheric science and service. This study proposes a new dynamical-statistical forecast method and a stable components dynamic statistical forecast (STsDSF) for subseasonal outgoing long-wave radiation (OLR) over the tropical Pacific region in January-February from 2004 to 2008. Compared with 11 advanced multi-model ensemble (MME) daily forecasts, the STsDSF model was able to capture the change characteristics of OLR better when the lead time was beyond 30 days in 2005 and 2006. The average pattern correlation coefficients (PCC) of STsDSF are 0.24 and 0.16 in 2005 and 2006, while MME is 0.10 and 0.05, respectively. In addition, the average value of PCC of the STsDSF model in five years is higher than MME in 7–11 pentads. Although both the STsDSF model and MME show a similar temporal correlation coefficient (TCC) pattern over the tropical Pacific region, the STsDSF model error grows more slowly than the MME error during 8–12 pentads in January 2005. This phenomenon demonstrates that STsDSF can reduce dynamical model error in some situations. According to the comparison of subseasonal forecasts between STsDSF and MME in five years, STsDSF model skill depends strictly on the predictability of the dynamical model. The STsDSF model shows some advantages when the dynamical model could not forecast well above a certain level. In this study, the STsDSF model can be used as an effective reference for subseasonal forecast and could feasibly be used in real-time forecast business in the future.

Exploring the Usefulness of Downscaling Free Forecasts from the Warn-on-Forecast System

The National Severe Storms Lab (NSSL) Warn-on-Forecast System (WoFS) is an experimental real-time rapidly-updating convection-allowing ensemble that provides probabilistic short-term thunderstorm forecasts. This study evaluates the impacts of reducing the forecast model horizontal grid spacing Δx from 3 km to 1.5 km on the WoFS deterministic and probabilistic forecast skill, using eleven case days selected from the 2020 NOAA Hazardous Weather Testbed (HWT) Spring Forecasting Experiment (SFE). Verification methods include (i) subjective forecaster impressions; (ii) a deterministic object-based technique that identifies forecast reflectivity and rotation track storm objects as contiguous local maxima in the composite reflectivity and updraft helicity fields, respectively, and matches them to observed storm objects; and (iii) a recently developed algorithm that matches observed mesocyclones to mesocyclone probability swath objects constructed from the full ensemble of rotation track objects. Reducing Δx fails to systematically improve deterministic skill in forecasting reflectivity object occurrence, as measured by critical success index (CSIDET), a metric that incorporates both probability of detection (PODDET) and false alarm ratio (FARDET). However, compared to the Δx = 3 km configuration, the Δx = 1.5 km WoFS shows improved mid-level mesocyclone detection, as evidenced by its statistically significant (i) higher CSIDET for deterministic mid-level rotation track objects and (ii) higher normalized area under the performance diagram curve (NAUPDC) score for probability swath objects. Comparison between Δx = 3 km and Δx = 1.5 km reflectivity object properties reveals that the latter have 30% stronger mean updraft speeds, 17% stronger median 80-m winds, 67% larger median hail diameter, and 28% higher median near-storm-maximum 0-3 km storm-relative helicity.

The Value of Meteorological Data in Optimizing the Pattern of Physical Load—A Forecast Model of Rowing Pacing Strategy

Mastering the information of arena environment is the premise for athletes to optimize their patterns of physical load. Therefore, improving the forecast accuracy of the arena conditions is an urgent task in competitive sports. This paper excavates the meteorological features that have great influence on outdoor events such as rowing and their influence on the pacing strategy. We selected the meteorological data of Tokyo from 1979 to 2020 to forecast the meteorology during the Tokyo 2021 Olympic Games, analyzed the athletes’ pacing choice under different temperatures, humidity and sports levels, and then recommend the best pacing strategy for rowing teams of China. The model proposed in this paper complements the absence of meteorological features in the arena environment assessment and provides an algorithm basis for improving the forecast performance of pacing strategies in outdoor sports.

Forecasting Model to Predict the Spreading of the COVID-19 Outbreak in Turkey

This study aimed to forecast the future of the COVID-19 outbreak parameters such as spreading, case fatality, and case recovery values based on the publicly available epidemiological data for Turkey. We first performed different forecasting methods including Facebook's Prophet, ARIMA and Decision Tree. Based on the metrics of MAPE and MAE, Facebook's Prophet has the most effective forecasting model. Then, using Facebook's Prophet, we generated a forecast model for the evolution of the outbreak in Turkey fifteen-days-ahead. Based on the reported confirmed cases, the simulations suggest that the total number of infected people could reach 4328083 (with lower and upper bounds of 3854261 and 4888611, respectively) by April 23, 2021. Simulation forecast shows that death toll could reach 35656 with lower and upper bounds of 34806 and 36246, respectively. Besides, our findings suggest that although more than 86.38% growth in recovered cases might be possible, the future active cases will also significantly increase compared to the current active cases. This time series analysis indicates an increase trend of the COVID-19 outbreak in Turkey in the near future. Altogether, the present study highlights the importance of an efficient data-driven forecast model analysis for the simulation of the pandemic transmission and hence for further implementation of essential interventions for COVID-19 outbreak.

Comparison of forecasting accuracy for Madden Julian Oscillation (MJO) and Convectively Coupled Equatorial Waves (CCEW) using Tropical Rainfall Measuring Mission (TRMM) and ERA-Interim precipitation forecast data for Indonesia

Tropical Rainfall Measuring Mission (TRMM) and ERA-Interim forecast data analyzed using second-order autoregressive AR(2) and space-time-spectra analysis methods (respectively) revealed contrasting results for predicting Madden Julian Oscillation (MJO) and Convectively Coupled Equatorial Waves (CCEW) phenomena over Indonesia. This research used the same 13-year series of daily TRMM 3B42 V7 derived datasets and ERA-Interim reanalysis model datasets from the European Center for Medium-Range Weather Forecasts (ECMWF) for precipitation forecasts. Three years (2016 to 2018) of the filtered 3B42 and ERA-Interim forecast data was then used to evaluate forecast accuracy by looking at correlation coefficients for forecast leads from day +1 through day +7. The results revealed that rainfall estimation data from 3B42 provides better results for the shorter forecast leads, particularly for MJO, equatorial Rossby (ER), mixed Rossby-gravity (MRG), and inertia-gravity phenomena in zonal wavenumber 1 (IG1), but gives poor correlation for Kelvin waves for all forecast leads. A consistent correlation for all waves was achieved from the filtered ERA-Interim precipitation forecast model, and although this was quite weak for the first forecast leads it did not reach a negative correlation in the later forecast leads except for IG1. Furthermore, Root Mean Square Error (RMSE) was also calculated to complement forecasting skills for both data sources, with the result that residual RMSE for the filtered ERA-Interim precipitation forecast was quite small during all forecast leads and for all wave types. These findings prove that the ERA-Interim precipitation forecast model remains an adequate precipitation model in the tropics for MJO and CCEW forecasting, specifically for Indonesia.

Canonical correlation analysis (CAA) model for long-range forecasts of sub-divisional monsoon rainfall over India

Using the canonical correlation analysis (CCA) approach, a forecast model for long range forecasts of monsoon (June-September) rainfall of 27 meteorological sub-divisions over India was developed, A set of 12 parameters, which have significant correlation with Indian monsoon rainfall, was used as predictors, The model was developed with the data of the period 1958-1994 and by retaining three significant canonical modes, The model showed useful predictive skill in of respect of meteorological sub-divisions over central parts of India and NW India with low errors and high skill scores for categorical forecasts, The model showed no predictive skill in respect of meteorological sub-division over south peninsula, Orissa, West Bengal and Bihar. The CCA model has been also found to perform better than another statistical model developed using the 12 same predictors, The CCA model also showed moderate skill in forecasting excess and deficient rainfall categories of sub-divisional monsoon rainfall during the extreme years.