Weather Forecast Information Research Articles

Evaluation of the responses of institutions and actors to the 2015/2016 El Niño drought in the Komati catchment in Southern Africa: lessons to support future drought management

Future climate projections for Southern Africa indicate an increase in the severity of droughts. Drought preparedness and management are important in such regions to minimise impacts on people and the environment. The aim of this study was to explore the responses of different institutions and actors to the 2015/2016 drought in the Komati catchment, and, consequently, what these experiences imply for drought management in the future. This was done through in-depth interviews with 30 key actors in the study area, including farmers, engineers, water managers and weather services personnel. Additionally, we examined data on precipitation, streamflows, reservoir levels, publications and communications in media, and minutes of the Komati Joint Operation Forum (KJOF) meetings. The results indicate that, whereas scientific tools for water resources management and drought forecasting exist in the catchment, the information generated using the tools was not sufficiently utilised in water (re) allocation decision making. This can be explained by the indifference of many stakeholders to early warning information, and by the preference of stakeholders to secure farm water supplies to minimize socio-economic losses in the short term. This resulted in late implementation of drought measures, when the drought had already started. Most of the measures implemented (i.e. water rationing and intensified monitoring) only helped partially in the crisis situation and did not tackle the root causes of vulnerability of the water users, especially for farmers. From the 2015/2016 drought we learnt that pro-active drought management could be supported through developing a drought management plan that is endorsed by all actors. More efforts are needed to improve how weather forecast information is packaged, communicated, deliberated and used by institutions (e.g. KJOF) and end-users (e.g. farmers). Moreover, this case study demonstrates that learning from past drought events is useful for improving evidence-based tools, policies and practices for drought management.

Accessing seasonal weather forecasts and drought prediction information for rural households in Chirumhanzu district, Zimbabwe.

Seasonal weather forecasts and drought hazard prediction through media sources and indigenous knowledge help provide an understanding of early warning systems and the preferred source information by rural households. This article focuses on the investigation of households’ access to weather forecasts and drought hazard prediction information as early warning to reduce drought risk on livelihood activities. The study was carried out in Chirumhanzu district, and the methods used for data collection included 217 household surveys, six focus group discussions, key informants’ interviews and document review. The study found that the majority of the households in the study area had access to seasonal weather forecast information (scientific), which almost half of the respondents received through radios. However, vulnerability to climate risks was exacerbated by seasonal weather forecasts, which were deemed by some households to be unreliable, inaccurate and not easily understood. In this regard, some households used indigenous knowledge to inform them on the status of the incoming rainy season and drought prediction. The use of indigenous knowledge depended on individuals’ ability to read and decode natural indicators of seasonal weather forecast and drought prediction. Indigenous knowledge is valuable for climate science as it enhances observations and interpretations on a larger spatial scale with considerable temporal depth by highlighting elements that are measured by climate science. Both scientific weather information and indigenous knowledge are important for seasonal weather forecasting and drought prediction, especially in rural settings, and complement each other if used and availed timely to households.

The impact of daily weather on retail sales: An empirical study in brick-and-mortar stores

In this study, we examine the influence of weather on daily sales in brick-and-mortar retailing using empirical data for 673 stores. We develop a random coefficient model that considers non-linear effects and seasonal differences using different weather parameters. In the ex-post analysis using historic weather data, we quantify the explanatory power of weather information on daily sales, identify store-specific effects and analyze the influence of specific sales themes. We find that the weather has generally a complex effect on daily sales while the magnitude and the direction of the weather effect depend on the store location and the sales theme. The effect on daily sales can be as high as 23.1% based on the store location and as high as 40.7% based on the sales theme. We also find that the impact of extreme bad and good weather occurrences can be misestimated by traditional models that do not consider non-linear effects. In the ex-ante analysis, we analyze if weather forecasts can be used to improve the daily sales forecast. We show that including weather forecast information improves sales forecast accuracy up to seven days ahead. However, the improvement of the forecast accuracy diminishes with a higher forecast horizon.

Assessment of cassava-melon intercrop-based farm practices under climate change variation in Isiala-Mbano L.G.A, Imo State, Nigeria

The study assessed cassava-melon intercrop-based farm practices used by farmers under the climate change variations in Isiala-Mbano, L.G.A. of Imo State, Nigeria. Specific objectives achieved were; socioeconomic characteristics of the farmers, various farm practice options used by farmers during the period and constraints faced by farmers while using farm practice options were identified. Lastly effects of farm practices on cassavamelon intercrop-based farms were determined. 12 communities were selected from 28 communities in the study area and simple random sampling technique was used to select 5 farmers from each community giving 60 farmers. Data were analyzed using descriptive statistics and regression models. Results showed that majority of the farmers were female (62%) and married (58%). Farm practice options used by farmers include; change in planting and harvesting dates, early rainfall planting, fertilizer and organic manure application, mulching, change in tillage operations and use of irrigation. The result of regression showed R2 and F-statistics of 48.8% and 3.372 respectively. R2 value of (0.488) implied that 48.8% variability in the yield of cassava-melon was explained by farm practices adopted by the farmers in the study area. Use of improved varieties, late planting, quantity of organic manure used, and mulching were significant and positively influenced crop yields. Fertilizer application was significant but showed negative influence on yield. However, inadequate skills in using farm practice options, limited access to weather forecast and poor information access to climate change conditions were among the major constraints faced by the farmers. It is recommended that farmers should improve on the use of farm cultural practices such as mulching, application of organic manure and increase access to information on climate change variations in order to improve crop yield.Keywords: Assessment, Farm practice, Cassava-melon, Intercrop, Climate change variations

Localization Applications of 3D-GIS Artificial Weather Modification Operational Command System in Fuxin, China

Based on the command and management requirements of weather modification operations in Fuxin City, China, a new generation of three-dimensional operation command system platform for new artificial weather modification was developed and developed using modern communication network technology. The system uses integrated three-dimensional geographic information system (3D-GIS), global positioning system (GPS) positioning and virtual reality technology to calculate, analyze and process the new basic geographic information and weather modification information vector data, generate real-time weather modification operations and guide products in Fuxin area, and realize the impact on labor. The three-dimensional, comprehensive and effective management of basic weather information, early warning and forecast information, disaster prevention and mitigation information and other decision support information provides technical support for scientifically and effectively managing and directing weather modification operations.

Understanding emergency manager forecast use in severe weather events.

For Emergency Managers (EMs), preparations for severe weather have always relied on accurate, well-communicated National Weather Service (NWS) forecasts. As part of their constant work to improve these forecasts, the NWS has recently begun to develop impact-based products that share forecast uncertainty information with EMs, including the Probabilistic Hazard Information (PHI) tool. However, there is a lack of research investigating what forecast uncertainty information EMs understand, and what information needs exist in the current communication paradigm. This study used the Critical Incident Technique to identify themes from incidents involving weather forecast information that went well, or not so well, from the perspective of the EMs responding to them. In total, 11 EMs from a variety of locales east of the Rockies were interviewed—six of whom were county-level, two city, two state, and one from a school district. We found that EMs sought increased forecast detail as a potential event approached in time and built relational trust in the NWS through repeated interactions. EMs had difficulty preparing for events when they did not have details of the expected impacts, or the likelihood of those impacts, for their regions. In summary, EMs are already starting to work in an uncertainty-friendly frame and could be responsive to the impact details and increased forecaster relations proposed with the PHI tool.

APLIKASI PRAKIRAAN CUACA WILAYAH BALI BERBASIS ANDROID

Weather forecast information is very important for tourism in Indonesia, especially Bali as one of the main tourist destinations. That information must be easily accessible by the users, as one of them through a mobile phone. In this research developed an application to showing the city weather forecast information and area tourist weather forecast information in Bali through mobile devices based on android operating system. The data sourced from Indonesian Agency for Meteorological, Climatological and Geophysics (BMKG). Generally, the system is divided into two parts, such as the server and client (user). Admin created in the form of web server by using HTML, PHP and JavaScript that used for the management of weather forecast data and convert data from Indonesian Agency for Meteorological, Climatological and Geophysics (BMKG), while on the client side is an application based on adroid to access the weather forecast data. In the design of the application, the planning begins with the analysis of system requirements, the implementation of coding until testing applications. The result of this research is an application of the city weather forecast information and area tourist weather forecast information in Bali which can be displayed on the mobile devices based on android operating system. Based on the result of the application testing with black box method overall funcional application has been running well.

Interoperability challenges in river discharge modelling: A cross domain application scenario

River discharge is a critical water cycle variable, as it integrates all the processes (e.g. runoff and evapotranspiration) occurring within a river basin and provides a hydrological output variable that can be readily measured. Its prediction is of invaluable help for many water-related tasks including water resources assessment and management, flood protection, and disaster mitigation. Observations of river discharge are important to calibrate and validate hydrological or coupled land, atmosphere and ocean models. This requires using datasets from different scientific domains (Water, Weather, etc.). Typically, such datasets are provided using different technological solutions. This complicates the integration of new hydrological data sources into application systems. Therefore, a considerable effort is often spent on data access issues instead of the actual scientific question.This paper describes the work performed to address multidisciplinary interoperability challenges related to river discharge modeling and validation. This includes definition and standardization of domain specific interoperability standards for hydrological data sharing and their support in global frameworks such as the Global Earth Observation System of Systems (GEOSS).The research was developed in the context of the EU FP7-funded project GEOWOW (GEOSS Interoperability for Weather, Ocean and Water), which implemented a “River Discharge” application scenario. This scenario demonstrates the combination of river discharge observations data from the Global Runoff Data Centre (GRDC) database and model outputs produced by the European Centre for Medium-Range Weather Forecasts (ECMWF) predicting river discharge based on weather forecast information in the context of the GEOSS.

Ultra-Short-Term Forecast of Photovoltaic Output Power under Fog and Haze Weather

Fog and haze (F-H) weather has been occurring frequently in China since 2012, which affects the output power of photovoltaic (PV) generation dramatically by directly weakening solar irradiance and aggravating dust deposition on PV panels. The ultra-short-term forecast method presented in this study would help to fully reflect the dual effects of F-H on PV output power. Aiming at the weakening effect on solar irradiance, estimation models of atmospheric aerosol optical depth (AOD) based on particle matter (PM) concentration were established with machine learning (ML) method, and the total irradiance received by PV panels was calculated based on simplified REST2 model. Aiming at the aggravating effect on dust deposition on PV panels, sample set of “cumulative PM concentration—efficiency reduction” was constructed through special measurement experiments, then the efficiency reduction under certain dust deposition state was estimated with similar-day choosing method. Based on photoelectric conversion model, PM concentration prediction and weather forecast information, ultra-short-term forecast of PV output power was realized. Experimental results proved the validity and feasibility of the presented forecast method.

Feasibility Study on Modeling of Flight Time Prediction Uncertainty using Flight and Meteorological Conditions

A precise flight time uncertainty prediction is expected to enable a more efficient air traffic management. According to an uncertainty propagation equation derived from a physical model, the flight time uncertainty increases as a function of ground speed and the variances of true air speed and head wind speed. These factors also depend on the flight and meteorological conditions. It is expected possible to improve the uncertainty prediction accuracy by appropriately taking these factors into its modeling. Through the clustering analysis of the actual flight and weather forecast data, the feasibility of the flight time uncertainty modeling using the flight speed and weather forecast information is clearly demonstrated.

Climate Change and Adaptation Strategies: Farmers Perception in Oyo State, Nigeria

Climate change and weather variations have been one of the major problems of agricultural productivity most especially among crop farmers in Nigeria. This study therefore assesses farmer’s perception and their adaptation strategies to climate change in Ogo-Oluwa local Government area of Oyo State Nigeria. The survey research design was adopted in selecting 113 respondents, while the instrument of data collection was a structured questionnaire. The data collection was analyzed using both descriptive and inferential statistics. Results showed that the majority (65.5%) of the respondents were males while about 69% of them are between the ages of 31 to 50 years old, majorities (76.0%) of the respondents were married and average household size was 6.0 persons, family labour was the major source of labour for the respondents, 57.5 per cent and 32.7 per cent of the respondents had farm size of 3- 4 acres and above 4 acres respectively. It also shows that majority (51.3%) earned less than N10, 000 per month while 20.4 % and 21.2 % earns between N10, 000 - N20, 000 and N21, 000- N30, 000 per month respectively. The study reveals that 39.8% of the farmers had long years of farming experiences between 21-30 years. The study also reveals that the majority (81.0%) of the respondents are aware of the changing climatic conditions in their community. Educational qualification of respondents, farmers income, farming experiences and access to extension services are positively and significantly influenced farmer’s perception of climate change in the study area. Majority of the farmers have adapted to climate change through diversification of crop types and varieties, changing planting dates among others also constraints to adaptation of climate change include lack of access to weather forecast information, high cost of improved and resistant varieties, and inadequate knowledge of coping strategies. The study therefore recommends among others a deliberate policy of government to increase farmer’s access to weather forecast information in the study area.

Climate Change Adaptation related Hindrances among Rice Farmers in Nepal: Farm Level Analysis

The objective of this study is to measure the hindrances of climate change adaptation among rural rice farmers in Nepal and its possible way forward. This study was done in seven districts, one district from each of seven provinces, where three from Terai region (i.e. Bara, Dang and Kailali) and four from Hilly region (i.e. Ilam, Sindhuli, Syangja and Surkhet) of Nepal. A structural questionnaire with both closed and open - ended questions were prepared and used to obtain required information from 773 rice farming households from the targeted study areas. Interviews were conducted for the crop year 2016 and for the main season rice cultivation in Nepal covering from June/July to October/November of each year (i.e., monsoonal cultivation of paddy). Results show that, factors such as inadequate operational capital, poor access to weather forecast and climate change information, inadequate awareness program on climate change from government and non-government agencies are the major barriers for over 90 percent of the farmers surveyed by this study. Similarly, about 80 percent of the sample surveyed farmers opined that high cost of improving seeds, fertilizers and irrigation, inadequate knowledge on coping mechanism or in building resilience and inadequate access to credit facilities are the major hindrances to them for addressing weather and Climate Change (CC) related vagaries. Hence, the empirical results drawn from this study suggest that there is an urgent need in Nepal for greater investment at agrarian sector to address these farmers’ level constraints and including supports for improving access to market and credit issues to farmers ( institutional and policy supports). Poor and ultra-poor farmers are more vulnerable from the vagaries of climate change, thereby immediate actions are needed from Ministry of Agriculture, and newly set up local government units in terms of more public investments at local and national level to enhance the climate change resilience of smallholding and poor farmers of Nepal.

PENERAPAN DATA MINING UNTUK PRAKIRAAN CUACA DI KOTA MALANG MENGGUNAKAN ALGORITMA ITERATIVE DICHOTOMISER TREE (ID3)

In the preparation of weather forecasts information there are several obstacles, involving many sources of data and weather forecasts relying on the ability of the forerunner, so that the interpretations produced may differ between forecasters because of their own experience. Differences in interpretation can confuse users and potentially reduce the quality of information submitted. Based on this problem the author intends to study the forecast model with data mining using ID3 algorithm to obtain the appropriate model so as to facilitate the process of weather analysis and forecast. In building the application, data obtained from BMKG website is addressed dataonline.bmkg.go.id and the data taken is datacuaca on January 1, 2012 through November 30, 2015 for a total of 2414 data and 9 attributes. After the selection of attributes (only weather related attributes) and removing damaged data (incomplete data and outliers), the data is reduced to 1790 and attributes reduced to 6 pieces. In the testing process performed with 179 data (10% of the data set) random, it is known that there are 112 databases corresponding to the actual weather data. So it can be concluded that the accuracy of 73.74%.

Micro-pulse polarization lidar at 1.5 μm using a single superconducting nanowire single-photon detector.

An all-fiber, eye-safe and micro-pulse polarization lidar is demonstrated with a polarization-maintaining structure, incorporating a single superconducting nanowire single-photon detector (SNSPD) at 1.5μm. The time-division multiplexing technique is used to achieve a calibration-free optical layout. A single piece of detector is used to detect the backscatter signals at two orthogonal states in an alternative sequence. Thus, regular calibration of the two detectors in traditional polarization lidars is avoided. The signal-to-noise ratio of the lidar is guaranteed by using an SNSPD, providing high detection efficiency and low dark count noise. The linear depolarization ratio (LDR) of the urban aerosol is observed horizontally over 48h in Hefei [N31°50'37'', E117°15'54''], when a heavy air pollution is spreading from the north to the central east of China. Phenomena of LDR bursts are detected at a location where a building is under construction. The lidar results show good agreement with the data detected from a sun photometer, a 532nm visibility lidar, and the weather forecast information.

WA-MAC: A weather adaptive MAC protocol in survivability-heterogeneous wireless sensor networks

Nowadays, sensor nodes are deployed to different environments to perform applications. Harsh environments such as rain or snow could damage the sensor nodes permanently. In this paper, a novel survivability-heterogeneous wireless sensor network composed of sensor nodes that is resistant to environmental detriments is designed. We for the first time study the medium access control protocol in such heterogeneous wireless sensor networks for data transmission under different weather conditions. We present a new Weather Adaptive receiver-initiated MAC protocol, called WA-MAC. WA-MAC can largely reduce idle listening time to save energy consumption for sensors during data transmission and provide a relative delay bound data delivery service by establishing appropriate rendezvous time between senders and receivers. Besides, by adopting weather forecast information, WA-MAC adjusts the data transmission process between different types of sensors to avoid packets being lost or delayed. Simulation results in ns-2 simulator show that WA-MAC can achieve the required relative delay bound, a low duty cycle and a high packet delivery ratio in the network, which outperforms other existing MAC protocols.

Use of NLP Techniques for an Enhanced Mobile Personal Assistant: The Case of Turkish

This article introduces a Turkish mobile assistant application which produces state-of-the art results for the Turkish language by using natural language processing (NLP) techniques. The voice-enabled mobile assistant application allows users to enter queries for nine pre-defined tasks; namely, making calls, sending sms messages and emails, getting directions, querying exchange rates, weather forecast and traffic information, searching on the internet and launching applications on the phone. Users’ queries are processed in a multi-stage approach (viz., NLP, query classification and parameter extraction). Either the requested task is performed or the requested information is displayed as the response of the application. The article presents the architecture of the introduced system, its comparison with some prominent mobile assistants as well as the newly created data resources (viz., two query datasets annotated for classification and parameter extraction, two specific datasets for domain adaptation of named entity recognition and syntactic parsing NLP modules) to be used in further research. The evaluations on the impact of NLP preprocessing layers to the query classification performances reveal that the added value by NLP may range from 0.2 to 10.7 percentage points depending on the preferred machine learning algorithm for the query classification stage. The impact of NLP for the parameter extraction stage is also crucial since the outputs of NLP modules are used systematically by the extraction rules. The overall performance of the introduced approach is measured as 70.8% which is very promising under the fact that the system is trained with very limited-size of annotated data. The technology introduced in this article is basically designed for the case of a mobile assistant but it can also be used for every voice-enabled control system to improve the user experience, such as smart homes or smart televisions.

Adoption and Dissemination Pathways for Climate-Smart Agriculture Technologies and Practices for Climate-Resilient Livelihoods in Lushoto, Northeast Tanzania

Smallholder farmers in East Africa need information and knowledge on appropriate climate-smart agriculture (CSA) practices, technologies, and institutional innovations in order to effectively adapt to changing climatic conditions and cope with climate variability. This paper assesses farmer adoption of climate-smart agricultural practices and innovation after being exposed to Farms of the Future Approach (FotF). First; we explore and assess the various CSA technologies and practices; including institutional innovations farmers are adopting. Second; we identify and document farmer learning and dissemination pathways that can enhance adoption of CSA technologies and practices. Third; we identify existing institutions that enhance adoption of CSA practices. We use household survey data, complemented by qualitative information from focus group discussions and key informant interviews. The results show farmers are adopting a variety of CSA technologies, practices, and institutional innovations to after participating in the FotF approach with use of improved crop varieties, agroforestry, and scientific weather forecast information cited as the main practices. To minimize their risks and reduce vulnerabilities, farmers are diversifying and integrating five to 10 CSA practices in one season. Matengo pits, SACCOs, and efficient energy stoves were adopted by very few farmers due to their high initial investment costs and unsuitability to the area. Ninety-eight percent of farmers reported that they receive agricultural information orally from a variety of sources including government extension workers, seed companies, researchers, traditional experts, neighbors, radio agricultural shows, religious groups, farmer groups, and family members. Lastly, farmers reported that the FotF approach is a useful tool that enabled them to interact with other farmers and learn new CSA practices and innovations. Suggested improvements to make on the FotF included include longer trip duration, increased number of farmer participants, and gender balance and age considerations to include youth.

Gene-Expression Programming for Short-Term Forecasting of Daily Reference Evapotranspiration Using Public Weather Forecast Information

This study aimed to forecast the daily reference evapotranspiration (ETo) using a gene-expression programming (GEP) algorithm with limited public weather forecast information over Gaoyou station, located in Jiangsu province, China. To calibrate and validate the gene-expression code, important meteorological data and weather forecast information were collected from the local meteorological station and public weather media, respectively. The GEP algebraic formulation was successfully constructed based only on daily minimum and maximum air temperature using the true FAO56 Penman-Monteith (PM) set as reference values. The performance of the models was then assessed using the correlation coefficient (R), root mean squared error (RMSE), root relative squared error (RRSE) and mean absolute error (MAE). The study demonstrated that GEP is able to calibrate ETo (all errors ≤0.990 mm/day, R = 0.832–0.866) and forecast the daily ETo with good accuracy (RMSE = 1.207 mm/day, MAE = 0.902 mm/day, RRSE = 0.629 mm/day, R = 0.777). The model accuracies slightly decreased over a 7-day forecast lead-time. These results suggest that the GEP algorithm can be considered as a deployable tool for ETo forecast to anticipate decision on short-term irrigation schedule in the study zone.

Model predictive control with feedback correction for optimal energy dispatch of a networked microgrid

The novel model predictive control with feedback correction designed in this paper aims to optimize the energy dispatch and minimize the operation costs of a microgrid, which contributes to the improvement of pollution emissions and economic growth. The microgrid communication is based on power line communication, thus more accurate prediction models of photovoltaic and wind power generations of a networked microgrid can be designed from weather forecast information transmitted by power line communication. The prediction model for micro gas turbines and the loads of a microgrid are also proposed for optimization of the model predictive control. The rolling optimization model is updated by the latest forecast information to get minimization costs and optimal energy dispatch. The feedback correction designs predictions of generation and loads prediction errors to give an adjustment of the prediction model. Then the energy optimization dispatch will be updated by the adjusted prediction, so the most optimal dispatch will be obtained. Finally, the data of a microgrid in the Zhejiang province is applied in simulation and the minimization costs are compared with ideal costs to verify the performance and effectiveness of the proposed model predictive control strategy.

3D soil water nowcasting using electromagnetic conductivity imaging and the ensemble Kalman filter

Mapping and immediate forecasting of soil water content (θ) and its movement can be challenging. Although inversion of apparent electrical conductivity (ECa) measured by electromagnetic induction to calculate depth-specific electrical conductivity (σ) has been used, it is difficult to apply it across a field. In this paper we use a calibration established along a transect, across a 3.94-ha field with varying soil texture, using an ensemble Kalman filter (EnKF) to monitor and nowcast the 3-dimensional θ dynamics on 16 separate days over a period of 38days. The EnKF combined a physical model fitted with θ measured by soil moisture sensors and an Artificial Neural Network model comprising σ generated by quasi-3d inversions of DUALEM-421S ECa data. Results showed that the distribution of θ was controlled by soil texture, topography, and vegetation. Soil water dried fastest at the beginning after the initial irrigation event and decreased with time and soil depth, which was consistent with classical soil drying theory and experiments. It was also found that the soil dried fastest in the loamy and duplex soils present in the field, which was attributable to deep drainage and preferential flow. It was concluded that the EnKF approach can be used to improve the irrigation efficiency by applying variable irrigation rates across the field. In addition, soil water status can be nowcasted across large spatial extents using this method with weather forecast information, which will provide guidance to farmers for real-time irrigation management.