Seasonal Weather Forecasts Research Articles

Anticipating the monsoon: the necessity and impossibility of the seasonal weather forecast for South Asia, 1886-1953.

This article examines the most controversial of the activities of the India Meteorological Department (IMD): long-term seasonal forecasting for the South Asian subcontinent. Under the pressure of recurrent famines, in 1886 the imperial IMD commenced annual issue of monsoon predictions several months in advance, focused on one variable: rainfall. This state service was new to global late nineteenth-century meteorology, attempted first and most rigorously in India. Successive IMD leaders adapted the forecast in light of scientific and infrastructural developments, continuously revising the underlying methods of its production. All methods failed to achieve accurate prevision. Nevertheless, the imperatives of economic administration, empire and public demand compelled IMD scientists to continue annual publication of this unreliable product. This article contends that the seasonal forecast is best understood as an enduring ritual of good governance in a monsoonal environment. Through analysis of newspaper controversies, it suggests that although the seasonal forecast was the most compelling justification for the IMD's imperial and global importance, its limitations undercut popular trust in modern meteorology. Finally, this case illustrates the centrality of 'tropical meteorology' to the historical development of modern atmospheric science.

Farmer Preparedness for Building Resilient Agri-Food Systems: Lessons From the 2015/2016 El Niño Drought in Malawi

Across sub-Saharan Africa, climate change is leading to increasingly erratic weather patterns that challenge farming practices, particularly for smallholder farmers. Preparing farmers for these changes and increasing their resilience to extreme weather events is critical for food security in areas where populations are increasing. The El Niño event of 2015/16 led to drought conditions in Malawi which are expected to become more normal in the future. This resulted in widespread crop failure and the need for external food aid. The experiences of Malawian farmers during this time creates an opportunity to identify areas where adaptations in land management practices as part of resilience building initiatives can prepare farmers for future climates. This paper presents results of household surveys and interviews of 201 farmers from a case study in southern Malawi. Half of the farmers surveyed practice Conservation Agriculture (CA), a Climate Smart Agriculture technology that increased resilience to this drought event. The majority of households relied on agriculture for all their livelihood streams, indicating that diversification away from sole dependence on agriculture would increase resilience. Our study shows that poorer, female farmers are less likely to practice CA than wealthier male farmers. Results also illustrate that while farmers had access to seasonal weather forecasts, a key tool to guide land preparation and planting, they remained reluctant to believe them or to amend cropping or land management practices. Agricultural extension services within Malawi can play a vital role in preparing farmers for future extreme weather events and ensuring forecast communication link to predicted agricultural impacts and land management actions for building resilience into agricultural systems. Extension services need to focus on supporting poorer female farmers to adopt CA practices and providing farmers with the tools and knowledge to respond effectively to seasonal and sub-seasonal climate information.

Water Availability, Crop Choices and Profitability of Farming: A Case Study of Mahakanumulla Tank Village

Village Tank Cascade Systems (VTCSs) were built in ancient Sri Lanka as self-reliant and climate-resilient agro-ecological systems. This study examined crop choices and profitability of farming under alternative rainfall regimes in a well-functioning tank village system in Mahakanumulla VTCS in Anuradhapura district. A Linear Programming (LP) model was developed to represent farming activities in the tank village for the 2018-19 Maha and 2019 Yala seasons using data gathered from secondary sources and a key informant survey. The baseline equilibrium was calibrated treating farmers as profit maximisers cultivating four types of land (uplands and lowlands in Maha and Yala seasons), two types of labour (hired and family), and twelve-monthly water constraints. The model was simulated under alternative rainfall regimes and technological interventions. The optimal crop mixes, farm profits, and shadow prices of resources associated with the baseline scenarios were compared with those of counterfactual scenarios. The results of the analysis indicated that the success of an intervention is determined by the rainfall regime. The analysis further showed that the introduction of a short-aged rice variety helps more in mitigating drought in a Maha season and the introduction of a traditional rice variety helps more in drought during Yala season. The positive effects of desiltation is quite large when a traditional rice variety is introduced in a Maha drought. Provision of seasonal weather forecasts, which will enable farmers to choose appropriate technological interventions, is recommended.

Development of reservoir operation rules using seasonal climate forecasts

ABSTRACT This study verified the suitability of using precipitation forecasts in defining operation rules for the Cruzeta reservoir in Rio Grande do Norte, Brazil. The operation rules were developed through reservoir operation simulation–optimization, using a genetic algorithm. The performance indicators were analyzed in five operation scenarios: standard operating policy (SOP), current reservoir rationing rule (C), rationing without forecast (R), rationing with forecast (RF), and rationing with perfect forecast (RPF). The SOP scenario better met the total demand but made the system very susceptible to supply collapse. The results of the RF and RPF scenarios showed better compliance with the priority demands and the total demand during the dry periods. Changing from RF to RPF scenario, there is a small improvement in the evaluation indexes. The use of rules integrating the seasonal weather forecast is thus recommended.

Assessing Groundwater Level with a Unified Seasonal Outlook and Hydrological Modeling Projection

Although the annual rainfall in Taiwan is high, water shortages still occasionally occur owing to its nonuniform temporal and spatial distribution. At these times, the groundwater is considered an acceptable alternative water source. Groundwater is of particular value because it is considered a clean and reliable source of fresh water. To prevent water scarcity, this study utilized seasonal forecasting by incorporating hydrological models to evaluate the seasonal groundwater level. The seasonal prospective issued by the Central Weather Bureau of Taiwan (CWB) was combined with weather generator data to construct seasonal weather forecasts as the input for hydrological models. A rainfall-runoff model, HEC-HMS, and a coupled groundwater and surface water model, WASH123D, were applied to simulate the seasonal groundwater levels. The Fengshan Creek basin in northern Taiwan was selected as a study site to test the proposed approach. The simulations demonstrated stability and feasibility, and the results agreed with the observed groundwater table. The calibrations indicated that the average errors of river stage were 0.850 for R2, 0.279 for root-mean-square error (RMSE), and 0.824 for efficiency coefficient (CE). The simulation also revealed that the simulated groundwater table corresponded with observed hydrographs very well (R2 of 0.607, RMSE of 0.282 m, and CE of 0.621). The parameters were verified in this study, and they were deemed practical and adequate for subsequent seasonal assessment. The seasonal forecast of 2018 at Guanxi station indicated that the 25th and 75th percentiles of simulated annual rainfall were within 1921–3285 mm and the actual annual rainfall was 2031 mm. Its seasonal rainfall outlook was around 30% accurate for forecasts of three consecutive months in 2018. Similarly, at Xinpu station, its seasonal rainfall outlook was about 40% accurate, and the amount of annual rainfall (1295 mm) was within the range of the 25th and 75th percentiles (1193–1852 mm). This revealed that the actual annual precipitations at both Guanxi and Xinpu station corresponded with the range of 25th and 75th percentiles of simulated rainfall, even if the accurate rate for the 3 month seasonal forecast had some error. The subsequent groundwater simulations were overestimated because the amount of actual rainfall was far lower than the average of the historical record in some dry season months. However, the amount of rainfall returned to normal values during the wet seasons, where the seasonal forecast and observation results were similar.

Effects of climate anomalies on warm-season low flows in Switzerland

Abstract. Switzerland has faced extended periods of low river flows in recent years (2003, 2011, 2015 and 2018), with major economic and environmental consequences. Understanding the origins of events like these is important for water resources management. In this work, we provide data illustrating the individual and joint contributions of precipitation and evapotranspiration to low flows in both typical and dry years. To quantify how weather drives low flows, we explore how deviations from mean seasonal climate conditions (i.e., climate anomalies) of precipitation and potential evapotranspiration correlate with the occurrence and magnitude of annual 7 d lowest flows (Qmin) during the warm season (May through November) across 380 Swiss catchments from 2000 through 2018. Most warm-season low flows followed periods of below-average precipitation and above-average potential evapotranspiration, and the lowest low flows resulted from both of these drivers acting together. Low-flow timing was spatially variable across Switzerland in all years, including the driest (2003, 2011, 2015 and 2018). Low flows in these driest years were associated with much longer-lasting climate anomalies than the ≤2 month anomalies which preceded typical warm-season low flows in other years. We found that snow water equivalent and winter precipitation totals only slightly influenced the magnitude and timing of warm-season low flows in low-elevation catchments across Switzerland. Our results provide insight into how precipitation and potential evapotranspiration jointly shape warm-season low flows across Switzerland and potentially aid in assessing low-flow risks in similar mountain regions using seasonal weather forecasts.

Poleward Excursions by the Himalayan Subtropical Jet Over the Past Four Centuries

AbstractSince the 1980s, the subtropical jet stream has generally moved poleward, but its behavior varies by region and season. Here we examine the interannual variability and trends in the latitudinal position of the spring subtropical jet over the Himalayas. During the modern period (1948 to 2018), the spring (March‐April‐May) jet is typically anchored immediately south of the Himalayas but has rarely (in 1956, 1971, 1984, and 1999) moved poleward to pass over Kyrgyzstan and northwest China. A tree‐ring reconstruction of the jet's latitude indicates that such poleward excursions may have become more frequent after 1950, but it is not clear whether that behavior is unprecedented within the past four centuries. These insights into the behavior of the Himalayan subtropical jet may improve seasonal weather forecasts for the region and provide a target for climate simulations to test whether the recent spate of excursions is unusual and due to anthropogenic warming.

Performance of seasonal forecasts of Douro and Port wine production

Wine production is intricately dependant on the evolution of weather conditions in a given year. Therefore, seasonal weather forecasts coupled with empirical wine production models can play a critical role in the short to medium-term management of vineyards and wineries. The implementation of suitable and timely adaptation measures based on predicted wine productions may contribute to risk reduction and improve efficiency. The performance of seasonal forecasts of wine production in the Portuguese Douro & Port wine region (D&P WR) is here assessed for the first time. This application may serve as a case study to be potentially extended to other wine regions. Here, we develop a predictive logistic model of wine production based on monthly mean air temperatures and monthly total precipitation, averaged over the periods of February–March, May–June, and July–September, complemented with an autoregressive component of wine productions. The wine production in the D&P WR during the period 1950–2017 (68 years) is keyed into three classes: low, normal and high production years. The model reveals a correct estimation ratio of approximately 3/4 for the full period, and 2/3 when applied to independent 10%-random subsamples. We then evaluate the performance of the ECMWF 7-month seasonal weather forecasts, issued from February to August, in predicting the meteorological conditions relevant for the wine production in the D&P WR. Overall, the performance is satisfactory for the meteorological predictors. As for the weather forecasts coupled with the wine production model, results reveal that forecasts from May to August are strikingly the best performing, as 1) more observed data is integrated into the empirical model and 2) the skill of seasonal forecasts for summer months is higher. The operational application of these forecasts in the D&P WR is already foreseen. Given the encouraging results, we believe this case study and the established methodology may be tested and adapted to other wine regions worldwide, with obvious benefits for the winemaking sector.

Multivariate Postprocessing Methods for High-Dimensional Seasonal Weather Forecasts

Seasonal weather forecasts are crucial for long-term planning in many practical situations and skillful forecasts may have substantial economic and humanitarian implications. Current seasonal forecasting models require statistical postprocessing of the output to correct systematic biases and unrealistic uncertainty assessments. We propose a multivariate postprocessing approach using covariance tapering, combined with a dimension reduction step based on principal component analysis for efficient computation. Our proposed technique can correctly and efficiently handle nonstationary, non-isotropic and negatively correlated spatial error patterns, and is applicable on a global scale. Further, a moving average approach to marginal postprocessing is shown to flexibly handle trends in biases caused by global warming, and short training periods. In an application to global sea surface temperature forecasts issued by the Norwegian climate prediction model, our proposed methodology is shown to outperform known reference methods. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.

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.

Optimal Irrigation with Perfect Weekly Forecasts versus Imperfect Seasonal Forecasts

AbstractThis study examines the impact of weekly and seasonal weather forecasts on the optimal irrigation scheduling problem. We compare stochastic and deterministic simulation-optimization framewo...

Skilful seasonal prediction of winter gas demand

In Britain, residential properties are predominantly heated using gas central heating systems. Ensuring a reliable supply of gas is therefore vital in protecting vulnerable sections of society from the adverse effects of cold weather. Ahead of the winter, the grid operator makes a prediction of gas demand to better anticipate possible conditions. Seasonal weather forecasts are not currently used to inform this demand prediction. Here we assess whether seasonal weather forecasts can skilfully predict the weather-driven component of both winter mean gas demand and the number of extreme gas demand days over the winter period. We find that both the mean and the number of extreme days are predicted with some skill from early November using seasonal forecasts of the large-scale atmospheric circulation (r > 0.5). Although temperature is most strongly correlated with gas demand, the more skilful prediction of the atmospheric circulation means it is a better predictor of demand. If seasonal weather forecasts are incorporated into pre-winter gas demand planning, they could help improve the security of gas supplies and reduce the impacts associated with extreme demand events.

Water use of Coffea arabica in open versus shaded systems under smallholder’s farm conditions in Eastern Uganda

Coffee cultivation is a major source of income in East Africa. Increasing temperatures and water scarcity related to climate change are becoming major challenges for coffee production. Therefore, there is an urgent need for sound scientific understanding of the functioning of current coffee cultivation systems and the potential of agroforestry as an adaptation strategy to climate change. In a smallholder coffee farm on Mt Elgon, Uganda, we assessed the effect of three coffee cultivation systems (i.e., Coffee-Open, Coffee-Banana and Coffee-Cordia) on (i) the coffee cultivation environment (e.g. microclimate and soil moisture), (ii) water consumption of coffee, (iii) water consumption of banana (Musa sp.) and Cordia africana and (iv) water competition or complementary use between coffee and shade tree species. To this end, we monitored sap flux density (Js) (g cm−2 hour-1) of coffee, banana and C. africana from March 2015 to April 2016, using Granier thermal dissipation method, along with microclimate, soil moisture and rainfall. Shaded systems reduced irradiance by 70% in Coffee-Cordia system and 58% in Coffee-Banana system compared to Coffee-Open system. Maximum temperatures and daily temperature amplitude were on average reduced by 4 °C in both shaded systems compared to Coffee-Open system. Soil water content (SWC) in shaded systems was reduced by 59% in Coffee-Cordia and 6% in Coffee-Banana compared to Coffee-Open. Daily water consumption of coffee plants was 1.2 ± 0.64 l d-1 and did not differ between systems. Water use of banana was 3.1 ± 1.8 l d-1 and 42 ± 40 l d-1 by C. africana. Coffee-Banana system had the largest daily transpiration rate, 0.9 ± 0.4 mm d-1 per ground area and 0.6 ± 0.4 mm d-1 per unit leaf area, followed by Coffee-Cordia with 0.37 ± 0.1 mm d-1 (per ground area), 0.36 ± 0.1 mm d-1 (per leaf area) and Coffee-Open 0.24 ± 0.1 mm d-1 (per ground area), 0.27 ± 0.1 mm d-1 (per leaf area). Our results showed that differences in microclimate and SWC between cultivation systems did not influence coffee water use during the monitored year. However, water competition between coffee and shade trees could likely occur in drier years, due to the reduced SWC presently observed in shaded systems. Further research is needed to explore the performance of management practices (mulching, pruning and thinning) in interaction with seasonal weather forecast and appropriate selection of shade species (provision of extra products, reduced water use, fast growth and root zone below 80 cm depth) to match the systems’ water requirements with expected soil water availability.

100 Years of Progress in Understanding the Stratosphere and Mesosphere

AbstractThe stratosphere contains ~17% of Earth’s atmospheric mass, but its existence was unknown until 1902. In the following decades our knowledge grew gradually as more observations of the stratosphere were made. In 1913 the ozone layer, which protects life from harmful ultraviolet radiation, was discovered. From ozone and water vapor observations, a first basic idea of a stratospheric general circulation was put forward. Since the 1950s our knowledge of the stratosphere and mesosphere has expanded rapidly, and the importance of this region in the climate system has become clear. With more observations, several new stratospheric phenomena have been discovered: the quasi-biennial oscillation, sudden stratospheric warmings, the Southern Hemisphere ozone hole, and surface weather impacts of stratospheric variability. None of these phenomena were anticipated by theory. Advances in theory have more often than not been prompted by unexplained phenomena seen in new stratospheric observations. From the 1960s onward, the importance of dynamical processes and the coupled stratosphere–troposphere circulation was realized. Since approximately 2000, better representations of the stratosphere—and even the mesosphere—have been included in climate and weather forecasting models. We now know that in order to produce accurate seasonal weather forecasts, and to predict long-term changes in climate and the future evolution of the ozone layer, models with a well-resolved stratosphere with realistic dynamics and chemistry are necessary.

Comparison of Various Stochastic Approaches for Irrigation Scheduling Using Seasonal Climate Forecasts

AbstractThis paper presents a modeling framework for real-time decision support for irrigation scheduling using probabilistic seasonal weather forecasts which are incorporated into a simulation-opt...

Evaluating the applicability of using daily forecasts from seasonal prediction systems (SPSs) for agriculture: a case study of Nepal’s Terai with the NCEP CFSv2

Ensemble forecasts from dynamic seasonal prediction systems (SPSs) have the potential to improve decision-making for crop management to help cope with interannual weather variability. Because the reliability of crop yield predictions based on seasonal weather forecasts depends on the quality of the forecasts, it is essential to evaluate forecasts prior to agricultural applications. This study analyses the potential of Climate Forecast System version 2 (CFSv2) in predicting the Indian summer monsoon (ISM) for producing meteorological variables relevant to crop modeling. The focus area was Nepal’s Terai region, and the local hindcasts were compared with weather station and reanalysis data. The results showed that the CFSv2 model accurately predicts monthly anomalies of daily maximum and minimum air temperature (Tmax and Tmin) as well as incoming total surface solar radiation (Srad). However, the daily climatologies of the respective CFSv2 hindcasts exhibit significant systematic biases compared to weather station data. The CFSv2 is less capable of predicting monthly precipitation anomalies and simulating the respective intra-seasonal variability over the growing season. Nevertheless, the observed daily climatologies of precipitation fall within the ensemble spread of the respective daily climatologies of CFSv2 hindcasts. These limitations in the CFSv2 seasonal forecasts, primarily in precipitation, restrict the potential application for predicting the interannual variability of crop yield associated with weather variability. Despite these limitations, ensemble averaging of the simulated yield using all CFSv2 members after applying bias correction may lead to satisfactory yield predictions.

Seasonal forecasting of green water components and crop yield of summer crops in Serbia and Austria.

A probabilistic crop forecast based on ensembles of crop model output estimates, presented here, offers an ensemble of possible realizations and probabilistic forecasts of green water components, crop yield and green water footprints (WFs) on seasonal scales for selected summer crops. The present paper presents results of an ongoing study related to the application of ensemble forecasting concepts in crop production. Seasonal forecasting of crop water use indicators (evapotranspiration (ET), water productivity, green WF) and yield of rainfed summer crops (maize, spring barley and sunflower), was performed using the AquaCrop model and ensemble weather forecast, provided by The European Centre for Medium-range Weather Forecast. The ensemble of estimates obtained was tested with observation-based simulations to assess the ability of seasonal weather forecasts to ensure that accuracy of the simulation results was the same as for those obtained using observed weather data. Best results are obtained for ensemble forecast for yield, ET, water productivity and green WF for sunflower in Novi Sad (Serbia) and maize in Groß-Enzersdorf (Austria) - average root mean square error (2006-2014) was <10% of observation-based values of selected variables. For variables yielding a probability distribution, capacity to reflect the distribution from which their outcomes will be drawn was tested using an Ignorance score. Average Ignorance score, for all locations, crops and variables varied from 1.49 (spring barley ET in Groß-Enzersdorf) to 3.35 (sunflower water productivity in Groß-Enzersdorf).

Usability of extended range and seasonal weather forecast in Indian agriculture

Usability of extended range and seasonal weather forecast in Indian agriculture

An analysis of Southeastern US prescribed burn weather windows: seasonal variability and El Niño associations

Fire plays an important role in wildland ecosystems, critical to sustaining biodiversity, wildlife habitat and ecosystem health. By area, 70% of US prescribed burns take place in the Southeast, where treatment objectives range widely and accomplishing them depends on finding specific weather conditions for the effective and controlled application of fire. The climatological variation of the preferred weather window is examined here using two weather model reanalyses, with focus on conditions critical to smoke dispersion and erratic fire behaviour. Large spatial gradients were evident in some months (e.g. 3× change across the Appalachian Mountains in winter). Over most of the Southeast, availability of preferred conditions in summer was several (up to 8) times less than in autumn or winter. We offer explanation for this variability in terms of the mean seasonal changes of key weather conditions (especially mixing height and transport wind). We also examine the interannual variability of the preferred weather window for linkage to the tropical Pacific (1979–2010). Associations with the subset of El Niño events identified by outgoing-longwave-radiation suggest skilful seasonal fire weather forecasts are feasible. Together, these findings offer a predictive tool to prioritise allocation of scarce prescribed fire resources and maximise annual area treated across this landscape.

Seasonal forecasting of green water components and crop yields of winter wheat in Serbia and Austria.

A probabilistic crop forecast based on ensembles of crop model output (CMO) estimates offers a myriad of possible realizations and probabilistic forecasts of green water components (precipitation and evapotranspiration), crop yields and green water footprints (GWFs) on monthly or seasonal scales. The present paper presents part of the results of an ongoing study related to the application of ensemble forecasting concepts for agricultural production. The methodology used to produce the ensemble CMO using the ensemble seasonal weather forecasts as the crop model input meteorological data without the perturbation of initial soil or crop conditions is presented and tested for accuracy, as are its results. The selected case study is for winter wheat growth in Austria and Serbia during the 2006-2014 period modelled with the SIRIUS crop model. The historical seasonal forecasts for a 6-month period (1 March-31 August) were collected for the period 2006-2014 and were assimilated from the European Centre for Medium-range Weather Forecast and the Meteorological Archival and Retrieval System. The seasonal ensemble forecasting results obtained for winter wheat phenology dynamics, yield and GWF showed a narrow range of estimates. These results indicate that the use of seasonal weather forecasting in agriculture and its applications for probabilistic crop forecasting can optimize field operations (e.g., soil cultivation, plant protection, fertilizing, irrigation) and takes advantage of the predictions of crop development and yield a few weeks or months in advance.