Improvements in Weather Forecasting Technique Using Cognitive Internet of Things

Forecasting the weather is a challenging task for human beings because of the unpredictable nature of the climate.However,effective forecasting is vital for the general growth of a country due to the significance of weather forecasting in science and technology.The primary motivation behind this work is to achieve a higher level of forecasting accuracy to avoid any damage.Currently,most weather forecasting work is based on initially observed numerical weather data that cannot fully cover the changing essence of the atmosphere.In this work,sensors are used to collect real-time data for a particular location to capture the varying nature of the atmosphere.Our solution can give the anticipated results with the least amount of human engagement by combining human intelligence and machine learning with the help of the cognitive Internet of Things.The Authors identified weatherrelated parameters such as temperature,humidity,wind speed,and rainfall and then applied cognitive data collection methods to train and validate their findings.In addition,the Authors have examined the efficacy of various machine learning algorithms by using them on both data sets i.e.,pre-recorded metrological data sets and live sensor data sets collected from multiple locations.The Authors noticed that the results were superior on the sensor data.The Authors developed ensemble learning model using stacked method that achieved 99.25%accuracy,99%recall,99%precision,and 99%F1-score for Sensor data.It also achieved 85%accuracy,86%recall,85%precision,and 86%F1 score for Australian rainfall data.

Meteorological observations and weather forecasting services of the CHINARE

By 2018, China had conducted 34 scientific explorations in Antarctica spearheaded by the Chinese National Antarctic Research Expedition(CHINARE). Since the first CHINARE over 30 years ago, considerable work has been undertaken to promote the development of techniques for the observation of surface and upper-air meteorological elements, and satellite image and data reception systems at Chinese Antarctic stations and onboard Chinese icebreakers have played critical roles in this endeavor. The upgrade of in situ and remote sensing measurement methods and the improvement of weather forecasting skill have enabled forecasters to achieve reliable on-site weather forecasting for the CHINARE. Nowadays, the routing of icebreakers, navigation of aircraft, and activities at Chinese Antarctic stations all benefit from the accurate weather forecasting service. In this paper, a review of the conventional meteorological measurement and operational weather forecasting services of the CHINARE is presented.

Machine Learning of Weather Forecasting Rules from Large Meteorological Data Bases

Discovery of useful forecasting rules from observational weather data is an outstanding interesting topic.The traditional methods of acquiring forecasting knowledge are manual analysis and investigation performed by human scientists.This paper presents the experimental results of an automatic machine learning system which derives forecasting rules from real observational data.We tested the system on the two large real data sets from the areas of centra! China and Victoria of Australia.The experimental results show that the forecasting rules discovered by the system are very competitive to human experts.The forecasting accuracy rates are 86.4% and 78% of the two data sets respectively

The Need of Incorporating Indigenous Knowledge Systems into Modern Weather Forecasting Methods

The study was aimed to examine the need of incorporating traditional weather forecasting renowned indigenous knowledge system (IKS) into modern weather forecasting methods to be used for planning farming activities. In addition, not only gap that is not infused by current weather forecasting system with their advanced studies to understand why it is incorporated into existing technical frameworks was regarded, but also the limitation of advanced weather forecasting approach and strength to be elicited by indigenous knowledge system are crucial. Perspicuously, forms and onsite interrogates have been conducted to assess people’s beliefs, understanding, and attitudes on the indigenous knowledge system significance on weather forecasting. Therefore, atmospheric and biological conditions, astronomic, as well as relief characteristics were used to predict the weather over short and long periods. Usually, in assessing weather conditions, the conduct of animals and insects were listed as essential. Obviously, in order to predict weather particularly from rain within about short period of time, astronomical characteristics were used. Commonly, there are few peers who know conventional weather prediction approaches. This lowers the reliability of conventional weather prediction. The findings revealed some variables that impact meteorological inaccuracy by scientific methods and help to recognize and evaluate the gap that current meteorological technologies do not achieve and new particulars anticipated to be filled with conventional methods to attain accurate weather prediction. Additionally, the study indicated that both modern and conventional processes have certain positive and limitations, which means that they can be coupled to generate more accurate weather prediction reports for end users.

How much Numerical Products Affect Weather Forecasting

The paper shows how much improvement can be achieved in weather forecasting by using NWP products. And for weather element forecasts, the types and number of NWP products highly impact on the quality of MOS forecasts and other utilities.

An Implementation of Full Cycle Strategy Using Dynamic Blending for Rapid Refresh Short-range Weather Forecasting in China

The partial cycle(PC)strategy has been used in many rapid refresh cycle systems(RRC)for regional short-range weather forecasting.Since the strategy periodically reinitializes the regional model(RM)from the global model(GM)forecasts to correct the large-scale drift,it has replaced the traditional full cycle(FC)strategy in many RRC systems.However,the extra spin-up in the PC strategy increases the computer burden on RRC and generates discontinuous smallscale systems among cycles.This study returns to the FC strategy but with initial fields generated by dynamic blending(DB)and data assimilation(DA).The DB ingests the time-varied large-scale information from the GM to the RM to generate less-biased background fields.Then the DA is performed.We applied the new FC strategy in a series of 7-day batch forecasts with the 3-hour cycle in July 2018,and February,April,and October 2019 over China using a Weather Research and Forecast(WRF)model-based RRC.A comparison shows that the new FC strategy results in less model bias than the PC strategy in most state variables and improves the forecast skills for moderate and light precipitation.The new FC strategy also allows the model to reach a balanced state earlier and gives favorable forecast continuity between adjacent cycles.Hence,this new FC strategy has potential to be applied in RRC forecast systems to replace the currently used PC strategy.

IMPACT OF VERTICAL RESOLUTION, MODEL TOP AND DATA ASSIMILATION ON WEATHER FORECASTING——A CASE STUDY

The impacts of stratospheric initial conditions and vertical resolution on the stratosphere by raising the model top,refining the vertical resolution,and the assimilation of operationally available observations,including conventional and satellite observations,on continental U.S.winter short-range weather forecasting,were investigated in this study.The initial and predicted wind and temperature profiles were analyzed against conventional observations.Generally,the initial wind and temperature bias profiles were better adjusted when a higher model top and refined vertical resolution were used.Negative impacts were also observed in both the initial wind and temperature profiles,over the lower troposphere.Different from the results by only raising the model top,the assimilation of operationally available observations led to significant improvements in both the troposphere and stratosphere initial conditions when a higher top was used.Predictions made with the adjusted stratospheric initial conditions and refined vertical resolutions showed generally better forecasting skill.The major improvements caused by raising the model top with refined vertical resolution,as well as those caused by data assimilation,were in both cases located in the tropopause and lower stratosphere.Negative impacts were also observed in the predicted near surface wind and lower-tropospheric temperature.These negative impacts were related to the uncertainties caused by more stratospheric information,as well as to some physical processes.A case study shows that when we raise the model top,put more vertical layers in stratosphere and apply data assimilation,the precipitation scores can be slightly improved.However,more analysis is needed due to uncertainties brought by data assimilation.

Weather Forecasting in China

Relying on advanced technology, China has greatly improved its meteorological service in the past few years. The modern meteorological technical system has played an important role in preventing and reducing damage from natural disasters, thus helping to advance China’s economic construction. Meanwhile, the field has benefitted from international cooperation on scientific research and meteorological education. Women meteorologists have been essential to this process.

A STUDY OF THE INFLUENCE OF MICROPHYSICAL PROCESSES ON TYPHOON NIDA(2016) USING A NEW DOUBLE-MOMENT MICROPHYSICS SCHEME IN THE WEATHER RESEARCH AND FORECASTING MODEL

The basic structure and cloud features of Typhoon Nida(2016) are simulated using a new microphysics scheme(Liuma) within the Weather Research and Forecasting(WRF) model. Typhoon characteristics simulated with the Liuma microphysics scheme are compared with observations and those simulated with a commonly-used microphysics scheme(WSM6). Results show that using different microphysics schemes does not significantly alter the track of the typhoon but does significantly affect the intensity and the cloud structure of the typhoon. Results also show that the vertical distribution of cloud hydrometeors and the horizontal distribution of peripheral rainband are affected by the microphysics scheme. The mixing ratios of rain water and graupel correlate highly with the vertical velocity component and equivalent potential temperature at the typhoon eye-wall region. According to the simulation with WSM 6 scheme,it is likely that the very low typhoon central pressure results from the positive feedback between hydrometeors and typhoon intensity. As the ice-phase hydrometeors are mostly graupel in the Liuma microphysics scheme, further improvement in this aspect is required.

Experiments in Forecasting Mesoscale Convective Weather over Changjiang Delta

The real time operational severe convective weather forecast experiment carried out during May to July in 1990 over the Changjiang Delta is briefly described. The heavy rainfall and severe conveetive weather forecast worksheets for the Changjiang Delta have been proposed and used in the daily forecasting. Results show that the ability of 0-12h convective weather prediction has been improved significantly after the development of the forecast methods and the establishment of a mesoscale forecast base at Shanghai Meteorological Center during 1986 to 1990.Three cases of convective weather systems (meso-alpha, meso-beta, meso-gamma) during the experiment period are described and discussed.

A Methodological Study on Using Weather Research and Forecasting(WRF) Model Outputs to Drive a One-Dimensional Cloud Model

A new method for driving a One-Dimensional Stratiform Cold （1DSC） cloud model with Weather Research and Fore casting （WRF） model outputs was developed by conducting numerical experiments for a typical large-scale stratiform rainfall event that took place on 4-5 July 2004 in Changchun, China. Sensitivity test results suggested that, with hydrometeor pro files extracted from the WRF outputs as the initial input, and with continuous updating of soundings and vertical velocities （including downdraft） derived from the WRF model, the new WRF-driven 1DSC modeling system （WRF-1DSC） was able to successfully reproduce both the generation and dissipation processes of the precipitation event. The simulated rainfall intensity showed a time-lag behind that observed, which could have been caused by simulation errors of soundings, vertical velocities and hydrometeor profiles in the WRF output. Taking into consideration the simulated and observed movement path of the precipitation system, a nearby grid point was found to possess more accurate environmental fields in terms of their similarity to those observed in Changchun Station. Using profiles from this nearby grid point, WRF-1DSC was able to repro duce a realistic precipitation pattern. This study demonstrates that 1D cloud-seeding models do indeed have the potential to predict realistic precipitation patterns when properly driven by accurate atmospheric profiles derived from a regional short range forecasting system, This opens a novel and important approach to developing an ensemble-based rain enhancement prediction and operation system under a probabilistic framework concept.

Real-Time Crop Prediction Based on Soil Fertility and Weather Forecast Using IoT and a Machine Learning Algorithm

The aim of this article is to assist farmers in making better crop selection decisions based on soil fertility and weather forecast through the use of IoT and AI (smart farming). To accomplish this, a prototype was developed capable of predicting the best suitable crop for a specific plot of land based on soil fertility and making recommendations based on weather forecast. Random Forest machine learning algorithm was used and trained with Jupyter in the Anaconda framework to achieve an accuracy of about 99%. Based on this process, IoT with the Message Queuing Telemetry Transport (MQTT) protocol, a machine learning algorithm, based on Random Forest, and weather forecast API for crop prediction and recommendations were used. The prototype accepts nitrogen, phosphorus, potassium, humidity, temperature and pH as input parameters from the IoT sensors, as well as the weather API for data forecasting. The approach was tested in a suburban area of Yaounde (Cameroon). Taking into account future meteorological parameters (rainfall, wind and temperature) in this project produced better recommendations and therefore better crop selection. All necessary results can be accessed from anywhere and at any time using the IoT system via a web browser.

Development of New Capabilities Using Machine Learning for Space Weather Prediction

With the development of space exploration and space environment measurements,the numerous observations of solar,solar wind,and near Earth space environment have been obtained in last 20 years.The accumulation of multiple data makes it possible to better use machine learning technique,which has achieved unforeseen results in industrial applications in last decades,for developing new approaches and models in space weather investigation and prediction.In this paper,the efforts on the forecasting methods for space weather indices,events,and parameters using machine learning are briefly introduced based on the study works in recent years.These investigations indicate that machine learning,especially deep learning technique can be used in automatic characteristic identification,solar eruption prediction,space weather forecasting for solar and geomagnetic indices,and modeling of space environment parameters.

A Hybrid Methodology for Short Term Temperature Forecasting

Developing a reliable weather forecasting model is a complicated task, as it requires heavy IT resources as well as heavy investments beyond the financial capabilities of most countries. In Lebanon, the prediction model used by the civil aviation weather service at Rafic Hariri International Airport in Beirut (BRHIA) is the ARPEGE model, (0.5) developed by the weather service in France. Unfortunately, forecasts provided by ARPEGE have been erroneous and biased by several factors such as the chaotic character of the physical modeling equations of some atmospheric phenomena (advection, convection, etc.) and the nature of the Lebanese topography. In this paper, we proposed the time series method ARIMA (Auto Regressive Integrated Moving Average) to forecast the minimum daily temperature and compared its result with ARPEGE. As a result, ARIMA method shows better mean accuracy (91%) over the numerical model ARPEGE (68%), for the prediction of five days in January 2017. Moreover, back to five months ago, in order to validate the accuracy of the proposed model, a simulation has been applied on the first five days of August 2016. Results have shown that the time series ARIMA method has offered better mean accuracy (98%) over the numerical model ARPEGE (89%) for the prediction of five days of August 2016. This paper discusses a multiprocessing approach applied to ARIMA in order to enhance the efficiency of ARIMA in terms of complexity and resources.

Probability Forecast of Regional Landslide Based on Numerical Weather Forecast

The regional forecast of landslide is one of the key points of hazard mitigation. It is also a hot and difficult point in research field. To solve this problem has become urgent task along with Chinese economy fast development. This paper analyzes the principle of regional landslide forecast and the factors for forecasting. The method of a combination of Information Value Model and Extension Model has been put forward to be as the forecast model. Using new result of Numerical Weather Foreeast Research and that combination model, we discuss the implementation feasibility of regional landslide forecast. Finally, with the help of Geographic Information System, an operation system for southwest of China landslide forecast has been developed. It can carry out regional landslide forecast daily and has been pilot run in NMC. Since this is the first time linking theoretical research with meteorological service, further works are needed to enhance it.

Sensitivity of Medium-Range Weather Forecasts to the Use of Reference Atmosphere

In this paper, the authors develop the earlier work of Chen Jiabin et al. (1986). In order to reduce spectral truncation errors, the reference atmosphere has been introduced in ECMWF model, and the spectrally-represented variables, temperature, geopotential height and orography, are replaced by their deviations from the reference atmosphere. Two modified semi- implicit schemes have been proposed to alleviate the computational instability due to the introduction of reference atmosphere. Concerning the deviation of surface geopotential height from reference atmosphere, an exact computational formulation has been used instead of the approximate one in the earlier work. To re duce aliasing errors in the computations of the deviation of the surface geopotential height, a spectral fit has been used slightly to modify the original Gaussian grid-point values of orography.A series of experiments has been performed in order to assess the impact of the reference atmosphere on ECMWF medium- range forecasts at the resolution T21, T42 and T63. The results we have obtained reveal that the reference atmosphere introduced in ECMWF spectral model is generally beneficial to the mean statistical scores of 1000-200 hPa height 10-day forecasts over the globe. In the Southern Hemisphere, it is a clear improvement for T21, T42 and T63 throughout the 10-day forecast period. In the Northern Hemisphere, the impact of the reference atmos phere on anomaly correlation is positive for resolution T21, a very slightly damaging at T42 and almost neutral at T63 in the range of day 1 to day 4. Beyond the day 4 there is a clear improvement at all resolutions.

A stamp based exploration framework for numerical weather forecast

Numerical weather simulation data usually comprises various meteorological variables, such as precipitation, temperature and pressure. In practical applications, data generated with several different numerical simulation models are usually used together by forecasters to generate the final forecast. However, it is difficult for forecasters to obtain a clear view of all the data due to its complexity. This has been a great limitation for domain experts to take advantage of all the data in their routine work. In order to help explore the multi-variate and multi-model data, we propose a stamp based exploration framework to assist domain experts in analyzing the data. The framework is used to assist domain experts in detecting the bias patterns between numerical simulation data and observation data. The exploration pipeline originates from a single meteorological variable and extends to multiple variables under the guidance of a designed stamp board. Regional data patterns can be detected by analyzing distinctive stamps on the board or generating extending stamps using the Boolean set operations. Experiment results show that some meteorological phenomena and regional data patterns can be easily detected through the exploration. These can help domain experts conduct the data analysis efficiently and further guide forecasters in producing reliable weather forecast.

Artificial Intelligence Based Solar Radiation Predictive Model Using Weather Forecasts

Solar energy has gained attention in the past two decades,since it is an effective renewable energy source that causes no harm to the environment.Solar Irradiation Prediction(SIP)is essential to plan,schedule,and manage photovoltaic power plants and grid-based power generation systems.Numerous models have been proposed for SIP in the literature while such studies demand huge volumes of weather data about the target location for a lengthy period of time.In this scenario,commonly available Artificial Intelligence(AI)technique can be trained over past values of irradiance as well as weatherrelated parameters such as temperature,humidity,wind speed,pressure,and precipitation.Therefore,in current study,the authors aimed at developing a solar irradiance prediction model by integrating big data analytics with AI models(BDAAI-SIP)using weather forecasting data.In order to perform long-term collection of weather data,Hadoop MapReduce tool is employed.The proposed solar irradiance prediction model operates on different stages.Primarily,data preprocessing take place using various sub processes such as data conversion,missing value replacement,and data normalization.Besides,Elman Neural Network(ENN),a type of feedforward neural network is also applied for predictive analysis.It is divided into input layer,hidden layer,loadbearing layer,and output layer.To overcome the insufficiency of ENN in choosing the value of weights and hidden layer neuron count,Mayfly Optimization(MFO)algorithm is applied.In order to validate the performance of the proposed model,a series of experiments was conducted.The experimental values infer that the proposed model outperformed other methods used for comparison.