Non-crossing Quantile Regression Neural Network as a Calibration Tool for Ensemble Weather Forecasts

Despite the maturity of ensemble numerical weather prediction(NWP),the resulting forecasts are still,more often than not,under-dispersed.As such,forecast calibration tools have become popular.Among those tools,quantile regression(QR)is highly competitive in terms of both flexibility and predictive performance.Nevertheless,a long-standing problem of QR is quantile crossing,which greatly limits the interpretability of QR-calibrated forecasts.On this point,this study proposes a non-crossing quantile regression neural network(NCQRNN),for calibrating ensemble NWP forecasts into a set of reliable quantile forecasts without crossing.The overarching design principle of NCQRNN is to add on top of the conventional QRNN structure another hidden layer,which imposes a non-decreasing mapping between the combined output from nodes of the last hidden layer to the nodes of the output layer,through a triangular weight matrix with positive entries.The empirical part of the work considers a solar irradiance case study,in which four years of ensemble irradiance forecasts at seven locations,issued by the European Centre for Medium-Range Weather Forecasts,are calibrated via NCQRNN,as well as via an eclectic mix of benchmarking models,ranging from the naïve climatology to the state-of-the-art deep-learning and other non-crossing models.Formal and stringent forecast verification suggests that the forecasts post-processed via NCQRNN attain the maximum sharpness subject to calibration,amongst all competitors.Furthermore,the proposed conception to resolve quantile crossing is remarkably simple yet general,and thus has broad applicability as it can be integrated with many shallow-and deep-learning-based neural networks.

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.

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.

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.

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.

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

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.

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.

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.

Progress in Climate Prediction and Weather Forecast Operations in China

The current status of weather forecasting and climate prediction, and the main progress China has made in recent years, are summarized in this paper. The characteristics and requirements of modern weather forecast operations are described briefly, and the significance of Numerical Weather Prediction （NWP） for future development is emphasized. Tile objectives and critical tasks for seamless short-term climate predic- tion that covers the extended-range （15 30 days）, monthly, seasonal, annual, interannual and interdecadal timescales, are proposed.

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.

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.

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 Assessment of Potential Economic Gain from Weather Forecast Based Irrigation Scheduling for Marginal Farmers in Karnataka, Southern State in India

This study is aimed to assess the usefulness of weather forecasts for irrigation scheduling in crops to economize water use. The short-term gains for the farmers come from reducing costs of irrigation with the help of advisory for when not to irrigate because rain is predicted (risk-free because the wrong forecast only delays irrigation within tolerance). Here, a quantitative assessment of saving (indirect income) if irrigation is avoided as rain is imminent (as per forecast), using a five-year archived forecast data over Karnataka state at hobli (a cluster of small villages) level is presented. Estimates showed that the economic benefits to the farmers from such advisories were significant. The potential gain in annual income from such forecast-based irrigation scheduling was of the order of 10% - 15%. Our analysis also indicated that the use of advisory by a small percentage of more than 10 million marginal farmers (landholding < 3 acres) in Karnataka could lead to huge cumulative savings of the order of many crores.

Study on Weather Forecast Indicators during Mowing Grass Periods in Inner Mongolia

Based on ground observation and remote sensing monitoring data,regions appropriate for mowing herbage in the dog days and autumn were predicted and assessed quantitatively,and regions appropriate for mowing herbage were divided into comparatively suitable,suitable and optimum areas for mowing herbage. Servicing products can be spread to agricultural and pastoral areas via short message service,broadcasting,radio and network in time,and good service effect can be obtained. However,under the effect of precision rate of medium-term prediction areas,the prediction of some rainfall areas deviates from the actual rainfall areas. After timely correction of weather prediction in 24 and 48 hours,pastoral weather forecast service in mowing herbage regions is improved obviously. Herbage mowed in the dog days is rich in nutrients,but it is rainy in the period,which brings certain difficulties to herbage mowing and airing. Therefore,herdsmen in various regions should pay more attention to local weather prediction and regulate mowing herbage periods in time,and autumn herbage can be mowed if the weather is not suitable for mowing herbage in the dog days.

Scientific Analysis of Adding Weather Heat Capacity &#176Ch in the Weather Forecast

This paper analyzes the current weather forecast in most countries on the defects and deficiencies of temperature forecasts. In addition, weather temperature and weather heat capacity were systematically analyzed in this paper. For this reason, the paper put forward a suggestion for the weather heat capacity (oCh) to be added in weather forecast. If the weather forecast augments the unit (oCh), the weather forecast would upgrade from one-dimensional to two-dimensional, and would make the temperature forecast more scientific, more reasonable and more accurately, which can serve the people and make economic construction better than before.

A Deep Learning Approach for Forecasting Thunderstorm Gusts in the Beijing–Tianjin–Hebei Region

Thunderstorm gusts are a common form of severe convective weather in the warm season in North China,and it is of great importance to correctly forecast them.At present,the forecasting of thunderstorm gusts is mainly based on traditional subjective methods,which fails to achieve high-resolution and high-frequency gridded forecasts based on multiple observation sources.In this paper,we propose a deep learning method called Thunderstorm Gusts TransU-net(TGTransUnet)to forecast thunderstorm gusts in North China based on multi-source gridded product data from the Institute of Urban Meteorology(IUM)with a lead time of 1 to 6 h.To determine the specific range of thunderstorm gusts,we combine three meteorological variables:radar reflectivity factor,lightning location,and 1-h maximum instantaneous wind speed from automatic weather stations(AWSs),and obtain a reasonable ground truth of thunderstorm gusts.Then,we transform the forecasting problem into an image-to-image problem in deep learning under the TG-TransUnet architecture,which is based on convolutional neural networks and a transformer.The analysis and forecast data of the enriched multi-source gridded comprehensive forecasting system for the period 2021–23 are then used as training,validation,and testing datasets.Finally,the performance of TG-TransUnet is compared with other methods.The results show that TG-TransUnet has the best prediction results at 1–6 h.The IUM is currently using this model to support the forecasting of thunderstorm gusts in North China.