Local Weather Forecasts Research Articles

Integrating user- and data-driven weather forecasts to develop legitimate, credible, and salient information services for smallholders in the Global South

Climate-related risks and variability pose significant challenges to the livelihoods and food security of smallholder farmers practicing rainfed agriculture. Many smallholders have limited access to weather information from climate services, and this information is often not tailored to their specific context and needs. Therefore, they rely on local ecological knowledge. This study utilizes the second generation of climate services, which provide demand-driven forecast information systems through mobile apps. We present three cases from agricultural communities in Guatemala, Bangladesh, and Ghana where we collaborated with farmers to develop local weather forecasts (LF) and combined them with scientific weather forecasts (SF) to create hybrid weather forecasts (HF). The integration of user-driven forecasts (LF) and data-driven forecasts (SF) enhances the legitimacy of the service, thereby increasing farmers’ trust and credibility by providing skilful forecasts. Furthermore, our results demonstrate that the hybrid weather forecast approach facilitates climate-smart, adaptive agricultural decision-making, enhancing the resilience and capacity of smallholder farmers in the Global South to adapt to a changing climate.

Diurnal and seasonal dynamics of the canopy‐layer urban heat island of Kuwait

AbstractThis study examines the temporal and spatial variability of near‐surface air temperature and the canopy layer urban heat island (UHICL) of Kuwait City. Observations collected at 12 locations across the country of Kuwait and for the period 2010–2022 are analysed on an hourly and 3‐hourly basis to provide monthly and diurnal insights of the city's UHICL characteristics. Research on Kuwait's UHICL was first conducted by Nasrallah et al. (International Journal of Climatology, 10, 401–405). Results presented here have been afforded the benefit of additional stations and more extensive data compared with the earlier study. Mean positive UHICL intensities, ranging from 1.1°C to 3.8°C at night, are observed consistently across all months, owing to the prevalence of clear skies from winter to summer. Negative UHICL intensities, indicating a typical daytime urban cool island (UCICL), are most prominent on summer days, exhibiting a mean hourly magnitude range between 0.6°C and 2.6°C that extends into the early parts of the evening. Heat and cool island effects are maintained up to wind speeds approaching 10 m s−1 at the urban periphery. A coastal site near the city demonstrates strong influences of the Arabian Gulf temperatures and associated sea and land breeze effects on UHICL development. The results can be used for comparison with other desert locales, in the evaluation of urban climate models, for urban planning policies and improving local weather forecasts. This study honours in memoriam Dr. Hassan Nasrallah, who produced the first UHICL study in the Arab World.

The performance of Climate Information Service in delivering scientific, local, and hybrid weather forecasts: A study case in Bangladesh

Access to reliable and skillful Climate Information Service (CIS) is crucial for smallholder farmers in Bangladesh to mitigate the impacts of rainfall variability and extremes. This study aims to systematically evaluate the performance of CIS in providing Scientific Forecast (SF) and Local Forecast (LF) to smallholders in Bangladesh. The results were then compared with farmers’ perceptions of the forecast accuracy. Additionally, the skill of a simple hybrid forecast (HF), which is an integrated system of SF and LF, was assessed using the ERA5 and ground observation datasets as benchmarks. The SF and LF data were obtained from the meteoblue hindcast and from the interview, respectively. The results indicate that, overall, LF exhibits slightly higher skill compared to SF when evaluated against the ERA5 dataset. The forecast performance, however, declines by almost half when the ground-based observations are used, associated with high false alarms. Farmers, on the other hand, perceived SF to possess superior performance compared to LF. This study demonstrates that combining the SF and LF into a simple HF yields higher forecast skill than either individual forecast, highlighting the importance of HF to deliver a reliable and trustworthy weather forecast.

Collaborative Exploration of Storm-Scale Probabilistic Guidance for NWS Forecast Operations

Abstract The operational utility of the NOAA National Severe Storm Laboratory’s storm-scale probabilistic Warn-on-Forecast System (WoFS) was examined across the watch-to-warning time frame in a virtual NOAA Hazardous Weather Testbed (HWT) experiment. Over four weeks, 16 NWS forecasters from local Weather Forecast Offices, the Storm Prediction Center, and the Weather Prediction Center participated in simulated forecasting tasks and focus groups. Bringing together multiple NWS entities to explore new guidance impacts on the broader forecast process is atypical of prior NOAA HWT experiments. This study therefore provides a framework for designing such a testbed experiment, including methodological and logistical considerations necessary to meet the needs of both local office and national center NWS participants. Furthermore, this study investigated two research questions: 1) How do forecasters envision WoFS guidance fitting into their existing forecast process? and 2) How could WoFS guidance be used most effectively across the current watch-to-warning forecast process? Content and thematic analyses were completed on flowcharts of operational workflows, real-time simulation interactions, and focus group activities and discussions. Participants reported numerous potential applications of WoFS, including improved coordination and consistency between local offices and national centers, enhanced hazard messaging, and improved operations planning. Challenges were also reported, including the knowledge and training required to incorporate WoFS guidance effectively and forecasters’ trust in new guidance and openness to change. The solutions identified to these challenges will take WoFS one step closer to transition, and in the meantime, improve the capabilities of WoFS for experimental use within the operational community. Significance Statement A first-of-its-kind experiment brought together forecasters from local weather forecast offices and national centers to examine the experimental Warn-on-Forecast System’s (WoFS’s) potential applications across watch-to-warning scales. This experiment demonstrated that WoFS can provide great benefit to forecasters, though a few challenges remain. Benefits provided by WoFS frequently overlap roles and responsibilities at local and national scales, suggesting the potential for enhanced cross-office collaboration. The challenges anticipated for WoFS operational use are far fewer than the benefits, and some solutions to these challenges are now being implemented. Finally, the mixed-methods experimental framework described herein also provides guidance for future collaborative experiments in testbed research that examine impacts of new technologies across NWS entities.

A Wind Field Reconstruction from Numerical Weather Prediction Data Based on a Meteo Particle Model

In the present work, a methodology for wind field reconstruction based on the Meteo Particle model (MPM) from numerical weather prediction (NWP) data is presented. The development of specific wind forecast services is a challenging research topic, in particular for what concerns the availability of accurate local weather forecasts in highly populated areas. Currently, even if NWP limited area models (LAMs) are run at a spatial resolution of about 1 km, this level of information is not sufficient for many applications; for example, to support drone operation in urban contexts. The coupling of the MPM with the NWP limited area model COSMO has been implemented in such a way that the MPM reads the NWP output over a selected area and provides wind values for the generic point considered for the investigation. The numerical results obtained reveal the good behavior of the method in reproducing the general trend of the wind speed, as also confirmed by the power spectra analysis. The MPM is able to step over the intrinsic limitations of the NWP model in terms of the spatial and temporal resolution, even if the MPM inherits the bias that inevitably affects the COSMO output.

Increasing Grid Integration of Wind Energy by using Ampacity Techniques

The grid integration of renewable energy, particularly in Spain, supposes an important problem to deal with for Distributor System Operators (DSO). Most of the times Wind Energy Farms are located in places that are far away from the transmission networks so they have to be integrated into distribution networks that are frequently operating close to their static rate. Current regulations make almost impossible to build new overhead lines so the increase of the capacity of the existing lines is a new target for the DSO. This paper is devoted to the analysis of a new methodology and monitoring system that have been developed to overcome the existing static rates by moving the operation point of the overhead lines close to their dynamic rate. This new rate is computed by using both the IEEE and CIGRÉ algorithms and a local weather forecast. The obtained results show that this approach can increase the capacity of the lines in a significant percentage.

Predictive Analytics of Air Temperature in Alaskan Permafrost Terrain Leveraging Two-Level Signal Decomposition and Deep Learning

Local weather forecasts in the Arctic outside of settlements are challenging due to the dearth of ground-level observation stations and high computational costs. During winter, these forecasts are critical to help prepare for potentially hazardous weather conditions, while in spring, these forecasts may be used to determine flood risk during annual snow melt. To this end, a hybrid VMD-WT-InceptionTime model is proposed for multi-horizon multivariate forecasting of remote-region temperatures in Alaska over short-term horizons (the next seven days). First, the Spearman correlation coefficient is employed to analyze the relationship between each input variable and the forecast target temperature. The most output-correlated input sequences are decomposed using variational mode decomposition (VMD) and, ultimately, wavelet transform (WT) to extract time-frequency patterns intrinsic in the raw inputs. The resulting sequences are fed into a deep InceptionTime model for short-term forecasting. This hybrid technique has been developed and evaluated using 35+ years of data from three locations in Alaska. Different experiments and performance benchmarks are conducted using deep learning models (e.g., Time Series Transformers, LSTM, MiniRocket), and statistical and conventional machine learning baselines (e.g., GBDT, SVR, ARIMA). All forecasting performances are assessed using four metrics: the root mean squared error, the mean absolute percentage error, the coefficient of determination, and the mean directional accuracy. Superior forecasting performance is achieved consistently using the proposed hybrid technique.

Exploring Hybrid Models For Short-Term Local Weather Forecasting in IoT Environment

This paper explores using and hybridizing simple prediction models to maximize the accuracy of local weather prediction while maintaining low computational effort and the need to process and acquire large volumes of data. A hybrid RF-LSTM model is proposed and evaluated in this research paper for the task of short-term local weather forecasting. The local weather stations are built within an acceptable radius of the measured area and are designed to provide a short period of forecasting - usually within one hour. The lack of local weather data might be problematic for an accurate short-term valuable prediction in sustainable applications like agriculture, transportation, energy management, and daily life. Weather forecasting is not trivial because of the non-linear nature of time series. Thus, traditional forecasting methods cannot predict the weather accurately. The advantage of the ARIMA model lies in forecasting the linear part, while the SVR model indicates the non-linear characteristic of the weather data. Both non-linear and linear approaches can represent the combined model. The hybrid ARIMA-SVR model strengthens the matched points of the ARIMA model and the SVR model in weather forecasting. The LSTM and random forest are both popular algorithms used for regression problems. LSTM is more suitable for tasks involving sequential data with long-term dependencies. Random Forest leverages the wisdom of crowds by combining multiple decision trees, providing robust predictions, and reducing overfitting. Hybrid Random forest-LSTM potentially leverages the robustness and feature importance of Random Forest along with the ability of LSTM to capture sequential dependencies. The comparison results show that the hybrid RF-LSTM model reduces the forecasting errors in metrics of MAE, R-squared, and RMSE. The proposed hybrid model can also capture the actual temperature trend in its prediction performance, which makes it even more relevant for many other possible decision-making steps in sustainable applications. Furthermore, this paper also proposes the design of a weather station based on a real-time edge IoT system. The RF-LSTM leverages the parallelized characteristics of each decision tree in the forest to accelerate the training process and faster inferences. Thus, the hybrid RF-LSTM model offers advantages in terms of faster execution speed and computational efficiency in both PC and Raspberry Pi boards. However, the RF-LSTM consumes the highest peak memory usage due to being a combination of two different models.

Validating an advanced smartphone application for thermal advising in cold environments

The ClimApp smartphone application was developed to merge meteorological forecast data with personal information for individualized and improved thermal warning during heat and cold stress and for indoor comfort in buildings. For cold environments, ClimApp predicts the personal thermal stress and strain by the use of the Insulation REQuired model that combines weather and personal physiological data with additional consideration of the Wind Chill index based on the local weather forecast. In this study, we validated the individualized ClimApp index relative to measurements and compared it with the Universal Temperature Climate Index (UTCI). To this aim, 55 participants (27 females) were exposed to at least 1 h in an outdoor environment of 10 °C or below (average 1.4 °C air temperature, 74.9% relative humidity, and 4.7 m/s air velocity) inputting their activity level and clothing insulation as instructed by ClimApp. The UTCI and ClimApp indices were calculated and compared to the participants’ perceived thermal sensation. The ClimApp index root mean square deviation (RMSD) was below the standard deviation of the perceived thermal sensation which indicates a valid prediction and the UTCI RMSD was higher than the standard deviation which indicates an invalid prediction. The correlation of ClimApp and UTCI to the perceived thermal sensation was statistically significant for both models.

Improved Analog Ensemble Formulation for 3-Hourly Precipitation Forecasts

Abstract Analog ensembles (AnEns) traditionally use a single numerical weather prediction (NWP) model to make a forecast, then search an archive to find a number of past similar forecasts (analogs) from that same model, and finally retrieve the actual observations corresponding to those past forecasts to serve as members of an ensemble forecast. This study investigates new statistical methods to combine analogs into ensemble forecasts and validates them for 3-hourly precipitation over the complex terrain of British Columbia, Canada. Applying the past analog error to the target forecast (instead of using the observations directly) reduces the AnEn dry bias and makes prediction of heavy-precipitation events probabilistically more reliable—typically the most impactful forecasts for society. Two variants of this new technique enable AnEn members to obtain values outside the distribution of the finite archived observational dataset—that is, they are theoretically capable of forecasting record events, whereas traditional analog methods cannot. While both variants similarly improve heavier precipitation events, one variant predicts measurable precipitation more often, which enhances accuracy during winter. A multimodel AnEn further improves predictive skill, albeit at higher computational cost. AnEn performance shows larger sensitivity to the grid spacing of the NWP than to the physics configuration. The final AnEn prediction system improves the skill and reliability of point forecasts across all precipitation intensities. Significance Statement The analog ensemble (AnEn) technique is a data-driven method that can improve local weather forecasts. It improves raw model forecasts using past similar model predictions and observations, reducing future forecast errors and providing probabilities for a range of possible outcomes. One limitation of AnEns is that they commonly tend to make rare-event (e.g., heavy precipitation) forecasts appear less extreme. Usually, heavier precipitation events have a higher impact on society and the economy. This study introduces two new AnEn techniques that make operational forecasts of both probabilities and most likely amounts more accurate for heavy precipitation.

High-Precision AI-Enabled Flood Prediction Integrating Local Sensor Data and 3rd Party Weather Forecast

Flooding risk is a threat to many sea-level cities and residential areas in the world. In the city Kristianstad in southern Sweden, a large number of sensors of different types have been deployed to monitor rain and other weather conditions, water levels at sea and lakes, ground water levels, and water flows in the city's storm-water and sewage systems. All the sensors are enabled by battery and wireless communication, and allow real-time data to be transferred and visualized on a cloud-based Internet of Things (IoT) portal. To better enable the system with capacity of foreseeing upcoming flooding threats and to allow early response from decision-makers, it is desired to build a real-time flood forecast system by utilizing the massive sensor data collected at the IoT portal and data from 3rd party weather forecast service. In this article, we have developed a smart flood forecast system using machine learning and artificial neural networks. The developed forecast system has successfully integrated data from multiple sources and can make accurate flood forecast at distributed locations for the coming days. After being successfully implemented as software product and integrated with the city's IoT portal, our developed flood forecast system has significantly extended the basic monitoring functions of the city's IoT infrastructure. This article presents the context of this work, the challenges that have been encountered during our development, our solutions and performance evaluation results. To the best of our knowledge, this is the first large-scale IoT-based real-time flood forecast system that has been enabled by artificial intelligence (AI) and deployed in real world.

Conceptualizing Confidence: A Multisited Qualitative Analysis in a Severe Weather Context

Abstract Confidence is a concept important to weather prediction, shaping how risk information is created, shared, understood, and acted upon. For forecasters in the National Weather Service (NWS) and their partners in public safety, confidence is central to their work, appearing frequently during their decision support services. While confidence has been examined in a variety of literatures, it is often addressed simplistically or as one of many variables in a study. It is rarely the object of study in and of itself, even less so in a naturalistic setting like an operational environment. To build a more robust knowledge of confidence and its many dimensions, we conducted a multisited ethnography of three interrelated sites central to tornado prediction and information dissemination, leading up to and during a cool-season tornado event. In partnership with collaborators from the NWS and emergency management, we simultaneously deployed to a National Center, a local Weather Forecast Office, and an emergency management office. This article explicates confidence from multiple social science theories, considering the scientific, data-based roots of confidence, as well as its affective, relational, and procedural origins. Our results show that confidence emerges in varied and complex ways and at different scales. Confidence can indicate one’s assessment of evidence and agreement (or lack) of it, beliefs about partners’ future behavior based on past experiences, and ritual interactions between offices that create patterned expectations. We argue for a more robust interdisciplinary analysis of confidence given how it shapes weather-related policies and practices, technologies, and communication strategies.

An Unsupervised Snow Segmentation Approach Based on Dual-Polarized Scattering Mechanism and Deep Neural Network

Distribution of snow and its melting is a critical factor affecting local weather, avalanche and flood forecasting, livelihood of people residing, and hydropower production. Most of the existing dry and wet snow identification methods were based on expensive quad-pol SAR with finite generalizability, while dual-pol SAR with larger coverage, longer time series and open availability has more advantages. In this study, an unsupervised algorithm for dry and wet snow discrimination, NSAE-WFCM, is proposed based on a variety of polarimetric features derived from H-α decomposition in dual-pol mode using C-band Sentinel-1 SAR data. NSAE-WFCM constructs a deep training network using the pixel neighborhood-based sparse autoencoder (NSAE) to optimize polarimetric parameters, and inputs reconstructed features with different weights into feature-weighted fuzzy C-means clustering (WFCM) to distinguish dry and wet snow for each underlying surface. Ground observation was carried out during the snow melting period of March 2021 in Altay, China, to validate dual-pol NSAE-WFCM method with an overall accuracy and kappa coefficient of 88.8% and 0.68, respectively. The results show that NSAE-WFCM’s accuracy is similar to that of the quad-pol SAR-based dry and wet snow result (90.0%), and significantly better than that of previously published approaches extended to dual-pol SAR, such as SVM (76.7%), H-α-Wishart (65.5%), SPAN-based threshold method (51.7%), and wet snow-based method (43.1%). Therefore, the NSAE-WFCM algorithm improves the ability to classify wet and dry snow based on dual-pol polarimetric features, overcomes the high dependence of existing methods on quad-pol SAR data, and reduces manual interpretation by using unsupervised clustering.

Spatial and temporal characteristics of near‐surface air temperature across local climate zones in a tropical city

AbstractThe present study examines the temporal and spatial variability of near‐surface air temperature and the canopy layer urban heat island (UHI) in Singapore. Observations collected at 20 locations across the island city‐state and during 6 years make this one of the most extensive studies carried out in a tropical city. Local climate zones (LCZs), defined as urban built and rural land cover types which produce a unique air temperature response, are used to standardize intersite comparison. The results show that the choice of the rural reference can affect the night‐time UHI magnitude by up to 2°C under “ideal” (dry, clear, calm) conditions. The most frequently observed median UHI magnitude increases from 2.8 during all‐weather to 3.7°C during “ideal” conditions, respectively. A seasonality is present with lowest (highest) mean all‐weather values of ~2.0°C (~3.3°C), observed during the wet (dry) December–January (April–October) period. Mean night‐time UHI intensity across seven built‐type LCZs ranges between 1.8 and 3.5°C (2.5 and 4.3°C) for all‐weather (“ideal”) conditions. Corresponding daily values are 1.1–2.3°C (1.5–2.7°C). The lowest (highest) night‐time magnitudes are associated with open low‐rise LCZ 6 (compact high‐rise LCZ 1) built type. In the middle of the day LCZ 1 can experience a cool island effect. The average UHI values presented here give an indication of the extra warmth experienced in the built‐up spaces of Singapore relative to one possible rural reference land cover type (scattered trees LCZ B). Highest daytime heat exposure is observed in LCZs 3 and 8 which are characterized by low building heights, high impervious surface fraction and lack of vegetation. The present results can be used to support the development and evaluation of urban climate models, develop urban planning policies and improve local weather forecasts and the delivery of integrated urban services.

Capacity Building Needs of Artisanal Fisherfolks in North Central, Nigeria

The study analyzed the capacity building needs of artisanal fisherfolks in North Central, Nigeria. The Data were collected with the use of a questionnaire and analysed using frequency, percentages, mean score and Correlation. The results showed that about 99.2% of the fishers were male and the mean age of the fishers was 43.9ears. The mean years of experience were 24.3. The mean quantity of catch per day was 17.9 Kg and 6 persons was the average household size. About 47.33 % of the respondents has a high knowledge level on improved fishing practices. The fisherfolks required capacity building in handling and transportation of captured fish (x ̅=6.59), use and repair of nets, cages and traps (mean=6.29), use and maintenance of multiple hooks on a line (x ̅=6.71), canoe use and maintenance (x ̅=6.74), use of chorkor smoker (x ̅=6.68), fish marketing and distribution (x ̅=6.65), credit acquisition (x ̅=5.92), maintaining personal and environmental health hygiene (x ̅=6.0) and local weather forecasting (x ̅=6.32). The highest ranked constraints affecting artisanal fishing practices were the high cost of fishing inputs and inadequate access to credit facilities (x ̅=2.72). The study therefore recommends the provision of training in the identified areas of capacity building needs and the provision of more frequent and effective extension services to the fisherfolks.

Food and Agricultural Organization (FAO) Model Analysis of Training Needs of Artisanal Fishers in Kwara State, Nigeria

The aim of the study is the Food and Agricultural Organisation (FAO) Model Analysis of Training Needs of Artisanal fishers in Kwara State, Nigeria. Data were collected with the use of a questionnaire and analysed using frequency count, percentages, mean score and Binary Logistic Regression. The food and Agriculture Organisation (FAO) Needs Assessment Model was used to determine the areas of training needs. Any task or skill that had a threshold score above 6.13 requires training intervention. The results revealed that all the respondents were male (100 %), 41.7 years was the average age of the fishers. about 89.3 % of the respondents were married, 45.4% had no formal education, the average years of experience was 26.9 years and 59.7 % of the respondents practiced farming as a supportive occupation. 19.2 kg was the mean quantity of fish caught per day and the mean household size was 8 persons. The fisherfolks required capacity building identification and sorting of fish (x ̅=6.26), handling and transportation of captured fish (x ̅=7.46), canoe use and maintenance (x ̅=6.76), use of chorkor smoker (x ̅=6.7), maintaining personal and environmental health hygiene (x ̅=7.29) and local weather forecasting (x ̅= 6.62). The determinants of training needs on Artisanal Fishing practices were years of fishing experience (p<0.05), income (p<0.05), household size (p<0.10), and membership of a fishing association (p<0.01). The highest ranked constraint affecting artisanal fishing practices was the of storage facilities (x ̅=2.62). The recommendations of the study are that there should be provision of training in the identified areas and provision of supports to the artisanal fisherfolks inform of inputs and credit facilities..

Effects of weather forecasting on indoor comfort and energy savings in office buildings

ObjectiveTo explore whether a climate services approach can enable both energy saving and indoor comfort. MethodsAfter analysis of the local weather forecast, the air conditioning temperature of a government building was set to be adjusted at 7:30, 11:00, and 17:00. The indoor temperature and humidity were measured by using a MAPS6.0 sensor, and thermal comfort was calculated using ISO 7730 standards. Multiple regression analysis was used to examine the correlation between the weather forecast and chiller energy consumption. ResultsAfter a three-stage air conditioning temperature control process was implemented, predicted mean vote values between 0 and 0.5 were achieved for 79.1% of the working hours in the experimental period. Outdoor and air conditioning temperatures were the most critical factors affecting the hourly energy consumption of the air conditioner and water-cooled chiller. With every 1 °C increase in the outdoor temperature, the hourly energy consumption increased by 6.57. Moreover, with every 1 °C increase in the air conditioner temperature, the hourly energy consumption decreased by 8.63. DiscussionA predicted mean vote value of >0.5 was recorded for 15.9% of the working hours; this may be related to high indoor humidity, weather forecast inaccuracies, and the air conditioner being turned on too late in the day. From July to October 2021, 8749 kW h of electricity were saved, and carbon emissions were reduced by 1405 kg. Regulating air conditioning temperatures through weather forecasts can achieve energy savings.

The usability of ClimApp: A personalized thermal stress warning tool

ClimApp was developed to assist individuals in reducing the risk of exposure to thermal stress and to supply the user with individualized advice and recommendations. The tool integrates several human thermal models and heat indices with the local weather forecast and goes on to provide users with a prediction of their thermal stress based on their activity level, clothing level, and heat acclimatization input. Despite its innovative and interactive functionality, ClimApp’s utility and ease of use should be considered to increase its usability. The usability of ClimApp was evaluated over two iterations: one usability lab test (n = 10) and one field test (n = 38) where first-time users completed tasks in ClimApp related to navigation, perceived ease of use, and perceived usefulness. Activity theory guided the analysis, and a directed qualitative content analysis was applied to evaluate the usability of ClimApp. The results suggest that there is room for improvement when assessing the universal design aspects, navigation, and information complexity, yet the participants perceived the tool as useful in the situated context.

Communicating Probability Information in Hurricane Forecasts: Assessing Statements that Forecasters Use on Social Media and Implications for Public Assessments of Reliability

Forecasters are responsible for predicting the weather and communicating risk with stakeholders and members of the public. This study investigates the statements that forecasters use to communicate probability information in hurricane forecasts and the impact these statements may have on how members of the public evaluate forecast reliability. We use messages on Twitter to descriptively analyze probability statements in forecasts leading up to Hurricanes Harvey, Irma, Maria, and Florence from forecasters in three different groups: the National Hurricane Center, local Weather Forecast Offices, and in the television broadcast community. We then use data from a representative survey of United States adults to assess how members of the public wish to receive probability information and the impact of information format on assessments of forecast reliability. Results from the descriptive analysis indicate forecasters overwhelmingly use words and phrases in place of numbers to communicate probability information. In addition, the words and phrases forecasters use are generally vague in nature -- they seldom include rank adjectives (e.g., “low” or “high”) to qualify blanket expressions of uncertainty (e.g., “there is a chance of flooding”). Results from the survey show members of the public generally prefer both words/phrases and numbers when receiving forecast information. They also show information format affects public judgments of forecast reliability; on average, people believe forecasts are more reliable when they include numeric probability information.

Air Temperature Prediction Using Different Datamining Approaches In Sulaymaniyah City In Iraq

Climate change has a historical impact at universal and local levels over the past era. Climate change is one of the greatest challenge issues in the globe for meteorological research. Air temperature estimation, in particular, has been measured as a significant feature in weather impression studies on industrial sectors, environmental, ecological, and agricultural. Accurately predicting air temperature guides to measure lifestyle, perform a key character for the government, industries, and public in development activities. In this paper, we investigate the use of various data mining approaches such as Support Vector Machine (SVM), Decision tree (DT), and Naïve Bayes for air temperature prediction within Sulaymaniyah City in Kurdistan, IRAQ. The metrological data is collected from the local Weather Forecast Department in the city within the range 2013 to 2018 inclusive. A dataset for the metrological data was developed and used to train the data mining algorithms. The proposed data mining algorithms were tested on the dataset to predict the air temperature and the performance of these algorithms were compared using standard performance metrics. Support vector machine has accomplished promising performance among using algorithms