Daily Weather Forecasts Research Articles

Identifying climatic risks and relevant adaptation strategies for selected smallholder farming regions, Limpopo Province, South Africa

ABSTRACT Due to low adaptive capacities and high reliance on weather-sensitive natural ecosystems for their livelihoods and food production, smallholder farmers are highly vulnerable to risks associated with climate change and variability. Such risks are location-specific; thus, to cope with them, smallholder farmers require tailored adaptation strategies. To support these farmers, this study aimed to identify climatic risks threatening sustainable smallholder farming in the Limpopo Province regions of Gavaza, Ga-Makanye and Giyani. Weather station records spanning up to 1980–2021 were used to identify climatic risks, for the October–April dryland maize growing season. Risks were identified based on temporal trends and/or interannual variability patterns for reference evapotranspiration and a suite of agriculturally relevant rainfall and temperature indices. Based on the identified risks, tailor-made adaptation strategies were devised for application by farmers within the study region. The results revealed the specific risks of growing seasons becoming shorter and increasingly hotter with highly irregular rainfall patterns. To adapt and improve agricultural productivity despite these prevailing climatic risks, farmers within the study region will benefit if they plant drought tolerant, early maturing and higher yielding crop varieties, apply soil water conservation techniques and make use of seasonal and daily weather forecasts to guide their decision-making.

Describing future UK winter precipitation in terms of changes in local circulation patterns

Social scientists have argued that good communication around risks in climate hazards requires information to be presented in a user-relevant way, allowing people to better understand the factors controlling those risks. We present a potentially useful way of doing this by explaining future UK winter precipitation in terms of changes in the frequency, and associated average rainfall, of local pressure patterns that people are familiar with through their use in daily weather forecasts. We apply this approach to a perturbed parameter ensemble (PPE) of coupled HadGEM3-GC3.05 simulations of the RCP8.5 emissions scenario, which formed part of the UK Climate Projections in 2018. The enhanced winter precipitation by 2050–99 is largely due to an increased tendency towards westerly and south-westerly conditions at the expense of northerly/easterly conditions. Daily precipitation is generally more intense, most notably for the south-westerlies. In turn, we show that the changes in the frequency of the pressure patterns are consistent with changes in larger scale drivers of winter circulation and our understanding of how they relate to each other; this should build user confidence in the projections. Across the PPE, these changes in pressure patterns are largely driven by changes in the strength of the stratospheric polar vortex; for most members the vortex strengthens over the twenty-first century, some beyond the CMIP6 range. The PPE only explores a fraction of the CMIP6 range of tropical amplification, another key driver. These two factors explain why the PPE is skewed towards exploring the more westerly side of the CMIP6 range, so that the PPE’s description of UK winter precipitation changes does not provide a full picture.

Novel data-driven optimal control methods for cost-effective brine treatment

This study presents a novel data-driven optimal control method to minimize the cost of Convection-Enhanced Evaporation (CEE) systems under time-varying weather conditions. CEE is the approach of evaporating water from saline films (brine) on packed evaporation surfaces by air convection. Here, operating variables (brine injection rate, brine temperature, and air speed) are actively controlled as a function of current ambient conditions and daily weather forecast. The controller optimizes the process operation variables based on a dataset consisting of Pareto fronts, obtained in advance by solving a set of optimization problems. Three optimal operation strategies are presented: (1) real-time selection of operating variables, (2) predictive scheduled operation, and (3) hybrid wind-fan operation. The effectiveness of the proposed strategies was assessed through two case studies with distinct geographical locations and weather conditions: Alamogordo, New Mexico, and Minneapolis, Minnesota. The results show significant cost-saving potential relative to static operation. Predictive scheduled operation resulted in an annual average operating costs of $0.91/m3 and $6.63/m3 in Alamogordo and Minneapolis, respectively, with the higher costs in Minneapolis a result of the added thermal energy required to prevent freezing in winter.

Improving Accuracy of Daily Weather Forecast Model at Soekarno-Hatta Airport Using BILSTM with SMOTE and ADASYN

Bidirectional LSTM (BiLSTM) is an extension of LSTM which can improve model efficiency and accuracy in classification scenarios based on time series data or longer time series data repeatedly. This research uses the BiLSTM algorithm to build a daily weather forecast model at Soekarno-Hatta Airport. The model built will assist forecasters in making weather forecasts on a local scale. This research is expected to be implemented and able to increase the verification value of Soekarno-Hatta Airport weather forecasts to support flight safety in Indonesia. The dataset used is hourly surface air weather parameter data (synoptic data) of Soekarno-Hatta Meteorological Station for the period January 2018 - December 2022. There is an imbalance in the data set, so the SMOTE and ADASYN techniques are used to handle the problem. The output of this research is weather conditions categorised into sunny, sunny cloudy, cloudy, light rain, moderate rain, heavy rain, and thunder rain. The results obtained will go through model verification and evaluation by finding the accuracy value by comparing the weather forecast model output with actual weather data using a multi-category contingency table. The BiLSTM - ADASYN model obtained the highest average accuracy value compared to other models, which was 83.2%.

The Meteorological Device: Literary Modernism, the Daily Weather Forecast and the Productions of Anxiety

The Meteorological Device: Literary Modernism, the Daily Weather Forecast and the Productions of Anxiety

Downscaling long lead time daily rainfall ensemble forecasts through deep learning

Skilful and localised daily weather forecasts for upcoming seasons are desired by climate-sensitive sectors. Various General circulation models routinely provide such long lead time ensemble forecasts, also known as seasonal climate forecasts (SCF), but require downscaling techniques to enhance their skills from historical observations. Traditional downscaling techniques, like quantile mapping (QM), learn empirical relationships from pre-engineered predictors. Deep-learning-based downscaling techniques automatically generate and select predictors but almost all of them focus on simplified situations where low-resolution images match well with high-resolution ones, which is not the case in ensemble forecasts. To downscale ensemble rainfall forecasts, we take a two-step procedure. We first choose a suitable deep learning model, very deep super-resolution (VDSR), from several outstanding candidates, based on an ensemble forecast skill metric, continuous ranked probability score (CRPS). Secondly, via incorporating other climate variables as extra input, we develop and finalise a very deep statistical downscaling (VDSD) model based on CRPS. Both VDSR and VDSD are tested on downscaling 60 km rainfall forecasts from the Australian Community Climate and Earth-System Simulator Seasonal model version 1 (ACCESS-S1) to 12 km with lead times up to 217 days. Leave-one-year-out testing results illustrate that VDSD has normally higher forecast accuracy and skill, measured by mean absolute error and CRPS respectively, than VDSR and QM. VDSD substantially improves ACCESS-S1 raw forecasts but does not always outperform climatology, a benchmark for SCFs. Many more research efforts are required on downscaling and climate modelling for skilful SCFs.

Determine Communication channels used by smallholder farmers to access climate services in Musanze district

There is a significant threat on the African continent caused by climate change and limited practical options for adaptation and mitigation. Food security is becoming a serious problem and the agricultural sector which is the backbone of the Rwandan economy is severely impacted by climate shocks. The Musanze district's smallholder farmers access and use climate services to lessen the effects of extreme events of weather and climate. The conceptual structure is one method used by Rwanda Meteorological Agency to provide farmers with climatic services. Farmers employ a variety of modes to acquire climate services from the service providers for the purposes of planning, adaptation, and mitigation. Researchers, development partners, Ministries/Agencies, and others get climate products, which are then made available to farmers to support them make informed decisions. The survey was also carried out by engaging smallholder farmers using open-ended questions from key-format interviews, providing thorough information on how climate services are very vital and essential to the farmers. The findings indicated that the most commonly used products in planning and decision-making of agricultural activities are monthly bulletins, impact-based early warning information, daily weather forecasts and seasonal forecasts. The age, farmer’s income and the availability of equipment’s for accessing climate services contributed greatly to the readiness to use climate information. The availability of climatic services contributed to better selection of seeds and timing of pests and disease prevention. The most widely used modes of accessing climate services were radio, television, social media, and short messaging services. The objective of the study is to determine communication channels used by smallholder farmers to access climate services.

DETAIL ENGINEERING DESIGN INFRASTRUKTUR TEKNOLOGI INFORMASI PERINGATAN DINI BANJIR DINAS PENGAIRAN PROVINSI ACEH

The complexity of life in the world is the most difficult to anticipate when natural phenomena occur in the form of rain, earthquakes, landslides, volcanic eruptions, and whirlwinds. The natural event that causes an area to become a natural disaster most often is flooding, basically the rain that occurs can be predicted in terms of intensity, rainfall, volume, and time of occurrence. BMKG as a government institution that researches natural phenomena usually provides daily weather forecasts and the information can be obtained on websites and online media, TV, social media and other media, the Nangroe Aceh Darussalam Provincial Irrigation Service which is responsible for planning, handling, and construction of irrigation and The use of irrigation is challenged to make innovations using information technology and incorporating technically designed hardware telemetry (DED) to combine it into a flood early warning system, especially in river flows close to residential areas so that it can anticipate or mitigate flood disasters massively and informatively. to local governments, communities, media and other parties with an interest in the main tasks and functions of rescue. In developing and combining hardware, software, and brainware for this flood early warning system, the main focus was the use of Hawk Eye, high resolution CCTV operated using Igismap software, and Dynamic software, which successfully identified garbage in rivers that caused puddles, and sent signals and data to the Command Center (EWS Command Center).

Daily Weather Forecasting Based on Deep Learning Model: A Case Study of Shenzhen City, China

Daily weather conditions are closely related to every field of production and life, and the forecasting of weather conditions plays an important role in social development. Based on the data characteristics of urban weather conditions, a deep learning network was designed to forecast urban weather conditions, and its feasibility was proved by experiments. In view of the non-stationary and seasonal fluctuation of the time series of daily weather conditions in Shenzhen from 2015 to 2019, empirical mode decomposition (EMD) was used to carry out the stationary processing for the daily minimum humidity, minimum pressure, maximum temperature, maximum pressure, maximum wind speed and minimum temperature. The decomposed components, residual sequence and original sequence were reconstructed according to the degree of relevance. On this basis, a long short-term memory (LSTM) neural network for the Shenzhen daily weather forecast was used, using the advantages of the LSTM model in time-series data processing, using the grid search algorithm to find the optimal combination of the above parameters and combining with the gradient descent optimization algorithm to find optimal weights and bias, so as to improve the prediction accuracy of Shenzhen weather characteristics. The experimental results show that our design of the EMD-LSTM model has higher forecasting precision and efficiency than traditional models, which provides new ideas for the weather forecast.

Meteonetwork: An Open Crowdsourced Weather Data System

Citizen science has shown great potential for bringing large groups of people closer to science, thanks in part to cooperation with universities and research centers. In this context, amateur weather networks played a major role in the last few decades thanks to a constant growth in technology. An example is given by the Meteonetwork association, born in 2002, and mainly composed by atmospheric science enthusiasts, who built up in time a huge weather station network in Italy. In recent years, they have enlarged their horizons over Europe, displaying real time observations and daily maps coming from both personal weather stations and official networks. This study described how Meteonetwork has set up an open crowdsourced weather data system, how data are validated, and which products are generated and freely accessible for scientists and stakeholders for their own purposes. Two concrete use cases were described as examples: the Weatherness Project, which selects a subnet of Meteonetwork data for biometeorological and health purposes and the data assimilation process implemented to improve the initial conditions into the WRF meteorological model for daily weather forecasts.

Co-production, uptake of weather and climate services, and welfare impacts on farmers in Senegal: A panel data approach

CONTEXTClimate change and variability have been identified as major threats to key sectors such as agriculture that drive economic growth and sustainable development in developing countries. The provision of tailored weather and climate information services (WCIS) that meet users' expectations can play an important role in helping decision makers adapt to and mitigate the effects of climate change and climate However, the evidence base showing the benefits of WCIS is still thin. OBJECTIVEThis study analyzes the impact of uptake of tailored seasonal and daily weather forecasts that are mediated by a multidisciplinary working group (MWG) on crop productivity and household income of smallholder farmers in Senegal. METHODSA two-season balanced dataset in combination with panel econometrics was used to explore the impact of uptake of weather and climate information on crop income for farmers in Senegal. The data were complemented by participatory surveys that provide richer contextual information to explain the causal pathways that link farmers' uptake with increased crop income for farmers with access to the MWG. RESULTS AND CONCLUSIONSResults show that the use of weather and climate information increased the value of crop produce by between 10 and 25% for farmers with access to an MWG. Coordinated platforms that involve diverse stakeholders like the MWGs play a critical role in co-producing weather and climate information that are more usable to farmers, thereby improving uptake and livelihoods. The impact pathways and implications for policy are discussed. SIGNIFICANCEThis study expands the limited evidence base on the role of weather and climate services in improving the livelihoods of smallholder by (i) going beyond the use of cross-sectional and using longitudinal data, (ii) analyzing causal impacts on farmer's livelihoods, i.e., crop yields and household income and (iii) using participatory approaches to better explain the causal impact pathways.

Reshaping the concept of transit-oriented development in response to public space overheating near the transit nodes of Tokyo

In Tokyo, the overheating of public spaces in summer is a challenging issue that needs to be resolved for the vibrant social life in this city to be sustained. The urbanisation of Tokyo is based on transit-oriented developments (TODs); this study focused on understanding the correlation between the urban heat island (UHI) effect and public spaces within TODs and on providing solutions for mitigating the overheating of such spaces. The onsite microclimatic data of 30 public spaces near transit nodes were compared with average daily weather forecast data to identify the overheating levels. The spatial structures of the selected areas were then analysed to identify possible planning issues that led to the overheating. Public spaces with high-overheating levels tended to have a lack of vegetation and to be small, dry, and blocked from the wind flow. Public spaces that were not overheated tended to be wide, green, and shaded. These spaces had a microclimate similar to the daily weather forecast. These findings were used to develop new planning guidelines for improving TODs, developing overheating mitigation strategies for public spaces, and identifying future directions for sustainable improvement of the urban structure.

Aerosol as a critical factor causing forecast biases of air temperature in global numerical weather prediction models

Weather prediction is essential to the daily life of human beings. Current numerical weather prediction models such as the Global Forecast System (GFS) are still subject to substantial forecast biases and rarely consider the impact of atmospheric aerosol, despite the consensus that aerosol is one of the most important sources of uncertainty in the climate system. Here we demonstrate that atmospheric aerosol is one of the important drivers biasing daily temperature prediction. By comparing observations and the GFS prediction, we find that the monthly-averaged bias in the 24-h temperature forecast varies between ± 1.5 °C in regions influenced by atmospheric aerosol. The biases depend on the properties of aerosol, the underlying land surface, and aerosol–cloud interactions over oceans. It is also revealed that forecast errors are rapidly magnified over time in regions featuring high aerosol loadings. Our study provides direct “observational” evidence of aerosol’s impacts on daily weather forecast, and bridges the gaps between the weather forecast and climate science regarding the understanding of the impact of atmospheric aerosol.

Subseasonal to Seasonal Prediction of Weather to Climate with Application to Tropical Cyclones

AbstractDemands are growing rapidly in the operational prediction and applications communities for forecasts that fill the gap between daily weather forecasts and seasonal climate outlooks. Recent scientific advances have identified sources of predictability on this time range, and modeling advances are leading to better forecasts. However, much remains to be done to further improve their skill and to develop new climate service forecast products to help countries and sectorial decision makers better manage weather risks and extremes and to adapt to climate change. This paper reviews the history and describes the main challenges and opportunities for the modeling and forecast‐applications communities to improve subseasonal to seasonal (S2S) forecasts and products, along with current developments catalyzed by the World Weather Research Programme and World Climate Research Programme's joint Sub‐Seasonal to Seasonal Prediction Project. The case of tropical cyclones is highlighted as an illustrative example of the points discussed.

Day-Ahead Photovoltaic Forecasting: A Comparison of the Most Effective Techniques

We compare the 24-hour ahead forecasting performance of two methods commonly used for the prediction of the power output of photovoltaic systems. Both methods are based on Artificial Neural Networks (ANN), which have been trained on the same dataset, thus enabling a much-needed homogeneous comparison currently lacking in the available literature. The dataset consists of an hourly series of simultaneous climatic and PV system parameters covering an entire year, and has been clustered to distinguish sunny from cloudy days and separately train the ANN. One forecasting method feeds only on the available dataset, while the other is a hybrid method as it relies upon the daily weather forecast. For sunny days, the first method shows a very good and stable prediction performance, with an almost constant Normalized Mean Absolute Error, NMAE%, in all cases (1% < NMAE% < 2%); the hybrid method shows an even better performance (NMAE% < 1%) for two of the days considered in this analysis, but overall a less stable performance (NMAE% > 2% and up to 5.3% for all the other cases). For cloudy days, the forecasting performance of both methods typically drops; the performance is rather stable for the method that does not use weather forecasts, while for the hybrid method it varies significantly for the days considered in the analysis.

An Expectile Factor Model for Day-ahead Wind Power Forecasting

Volatile renewable power sources will soon dominate the power portfolio of leading industrial countries to match the goals of the Paris climate accord. Wind power is the leading renewable energy source. Its production is strongly volatile due to weather dependency and highly decentralised to various locations across a country. Hence, transmission and distribution system operators (TSO, DSO) and energy market participants are exposed to volume risk. Good forecasts of future wind power production are required to match residual load supply and demand in the network at any point time to circumvent load failure or frequency jumps. We propose a flexible methodology that can be scaled from a small wind farm data set to a large fleet size to forecast aggregated, turbine or farm specific wind power production. The methodology is based on multivariate expectile regressions using a factor model in combination with a vector autoregressive (VAR) time series framework with exogenous variables for forecasting purposes. Moreover, the methodology can be extended to deploy spatial information of various wind farms. We perform our analysis on a farm-level and an aggregated TSO-level data set. Results indicate out-performance of our candidate models for one day ahead forecasts. Moreover, we show that applying our candidate model affordable daily weather forecasts give better results than employing expensive intra-day weather forecasts. The model performs similarly well for farm-level data as well as for aggregated data.

An extension approach to close the gap between suppliers and users of agrometeorological services in the South-Western Free State of South Africa

Agrometeorological information, advisories and services remains one of the major hindrances toward productive agriculture. Well researched and scientifically proven information is inaccessible to most potential farmers whether commercial, resource poor or subsistence. A noticeable gap exists between information developers or suppliers and end-users such as farmers. This paper is aimed at discussing the existing gaps and practical solutions toward the enhancement of proper information dissemination. In workshops organised by the Agricultural Research Council in partnership with the University of the Free State on the training of extension agents the following was discovered: the interviewed extension agents indicated that 13% had good background on the application of agrometeorological information and 87% had no skill. About 80% followed the daily weather forecast. But 86% of the participants could not differentiate between short term forecast, seasonal and long-term predictions. The need for intensive training of extension agents on agrometeorological information, advisories and services was emphasised, since such information is the integral part of agricultural decision-making toward sustainable agricultural productivity and food security. Keywords: Extension Approach, Agrometeorology, Advisories, south-western Free State

A hybrid method for forecasting the energy output of photovoltaic systems

The intermittent nature of solar energy poses many challenges to renewable energy system operators in terms of operational planning and scheduling. Predicting the output of photovoltaic systems is therefore essential for managing the operation and assessing the economic performance of power systems. This paper presents a new technique for forecasting the 24-h ahead stochastic energy output of photovoltaic systems based on the daily weather forecasts. A comparison of the performances of the hybrid technique with conventional linear regression and artificial neural network models has also been reported. Initially, three single-stage models were designed, namely the generalized regression neural network, feedforward neural network and multiple linear regression. Subsequently, a hybrid-modeling approach was adopted by applying stepwise regression to select input variables of greater importance. These variables were then fed to the single-stage models resulting in three hybrid models. They were then validated by comparing the forecasts of the models with measured dataset from an operational photovoltaic system. The accuracy of the each model was evaluated based on the correlation coefficient, mean absolute error, mean bias error and root mean square error values. Simulation results revealed that the hybrid models perform better than their corresponding single-stage models. Stepwise regression-feedforward neural network hybrid model outperformed the other models with root mean square error, mean absolute error, mean bias error and correlation coefficient values of 2.74, 2.09, 0.01 and 0.932 respectively. The simplified network architecture of the hybrid schemes suggests that they are promising photovoltaic output prediction tools, particularly in locations where few meteorological parameters are monitored.

Simulating the Daily Evolution of West African Monsoon Using High Resolution Regional Cosmo-model: A Case Study of the First Half of 2015 over Nigeria

Understanding the dynamics and variability of the West African Monsoon (WAM) at daily time scales will improve skillful prediction of the onset and evolution of the monsoon and thus would contribute toward food security of Nigeria. This study, therefore, uses high resolution regional COSMO-model, a weather-mode model from the German Weather Service adopted by the Nigerian Meteorological Agency, to study the daily evolution of WAM as well as the ability of the model to predict the daily characteristics of monsoon, for the first half of 2015, over Nigeria. Results show that, qualitatively, the model has the ability to predict the daily evolution of WAM, daily variability of rainfall, which includes the onset of the raining season as well as dry-spells, over Nigeria. The spatial correlations between the observation and the forecast are generally greater than 0.64, implying that the model, though, underestimates the rainfall amount as much as half of the actual amount, it nevertheless proved to have a good representation of the spatial characteristics of the rain over Nigeria. The model shows that the Inter-Tropical Discontinuity (ITD) advances northward, from the Gulf of Guinea (GOG) to the Sahelian region, by about 0.42° per week; and that for the onset of monsoon in Nigeria, the average position of the ITD should be at least 6.7°N and must not retreat south of it in the subsequent average weekly position. In agreement with earlier findings, the model also shows that the African Easterly Jet (AEJ), together with its associated core, is not only a boreal summer element but can also exist during the boreal winter with the same strength in the wind speed. The atmospheric thermodynamic properties, predicted by the model, show that for an onset of the rains, a threshold value of at least 1500 J/Kg of convective available potential energy (CAPE) may be required. The results suggest that COSMO-Model has proved to be a good tool for operational daily weather forecast; therefore, the model could also have potential for seasonal rainfall predictions over Nigeria when run in climate mode.

Climate Change Impacts, Adaptation and Coping Strategies at Malindza, a Rural Semi-Arid Area in Swaziland

The objective of the study was to assess the impacts of climate change faced by rural households in the lowveld of Swaziland, using Malindza as a case study area, and further identify adaptation and coping strategies employed by households. A questionnaire was developed and used to conduct interviews from 160 households randomly selected in four rural communities of the study area. Data were analysed with SPSS software, and reported in forms of tables and figures. More or less all the respondents (99%) were aware of climate change and climate change variability, Sources of information included radios (92.5%), television (5.6%) and agricultural extension officers (2%). The information was however considered inadequate and of short term remedy as it was in the form of daily weather forecast. The perceived effects of climate change included crop failure (99%), loss of livestock (99%) and drying of surface water (99%). Only 9% of the households harvested enough maize to last for a year, and the rest (91%) had to rely on buying maize, exchanging it for labour or receiving food aid. The climate change adaptation strategies practiced included contour ploughing (49%), use of organic fertilisers (29%) and crop rotation (20%). Thirty two percent of the households planted hybrid maize seeds and 15% planted open pollinated maize seeds. Another 26% planted both hybrid maize and open pollinated maize seeds. On the other hand, coping strategies practiced included selling or consuming small livestock and chicken (97%), consuming maize left for seeds (93%) and reducing food intake (23%). It was clear that the effects of climate change in rural areas were severe and needed to be addressed before critical damages like loss of human life manifest. The government should ensure that farmer’s knowledge on climate change and variability is increased through education to improve their adaptive capacity.