Unknown Weather Research Articles

Enhancing in-season yield forecast accuracy for film-mulched wheat: A hybrid approach coupling crop model and UAV remote-sensing data by ensemble learning technique

Accurate in-season yield forecasts for field-scale crops are crucial for both farmers and decision-makers. Common methods for yield prediction are limited by the availability of unknown weather data (process-based crop models) and the failure to consider yield formation processes (statistical models based on unmanned aerial vehicle (UAV) images), respectively. Furthermore, previous studies focused only on crops without mulching, yet mulching is an important agronomic approach to increase grain yield in the arid areas of northwest China. We aim to develop a hybrid approach coupling crop model and UAV data through ensemble learning to achieve in-season yield forecasts for film-mulched wheat. A four-year field experiment was constructed (2018–2020 and 2021–2023). We first calibrated AquaCrop using data from 2018 to 2020, and historical weather data were employed to drive AquaCrop for predicting yields in 2021–2023. Next, statistical models were constructed to predict yields based on spectral and textural indices calculated from UAV images. Finally, a hybrid approach coupling the AquaCrop model and remote-sensing data was developed using ensemble learning technique. Quantifying the relative contribution of features used SHapley Additive exPlanations values. The results indicated that AquaCrop yield forecasts exhibited considerable uncertainties (R2: 0.53–0.63; NRMSE: 16.54%–14.83%). The interpretation of yield for remote-sensing data was influenced by background and saturation effects, reaching its highest accuracy at the heading stage (R2 was 0.80, NRMSE was 11.88%). Ensemble learning demonstrated strong performance compared to machine learning algorithms. The coupling model combined the advantages of crop and statistical models by the ensemble learning algorithm, achieving accurate yield predictions more than 40 days before harvest (heading stage) based on AdaBoost regression (R2 was 0.88, NRMSE was 8.40%). The most important forecasting factors affecting yield prediction were the textural indices, followed by the AquaCrop simulated values. Overall, the coupled model showed good performance in predicting the in-season yield of film-mulched wheat, which provided new insights into farm-scale yield prediction. Further validation of the generalizability of the coupled model in different scenarios is required in the future to improve the applicability of the model in actual production practice.

Simulation model calibration for a multi-purpose building on an hourly basis

When developing digital twins for buildings, the calibration of simulation models on an hourly basis is essential to maintain the fidelity of the virtual representation and to enable real-time monitoring and analysis in the operational phase. Achieving such a high accuracy in building performance simulations (BPS) calls for novel calibration strategies with enhanced effectiveness. In this regard, this paper outlines a calibration strategy that makes use of hourly measurements to improve the fidelity of energy simulation models. The proposed approach includes a hierarchical structure involving data acquisition and management, setting unknown weather parameters, sensitivity analysis, calibration of fixed parameters, and hourly calibration of dynamic variables. Here, acquired data from the building’s sensor are refined to enable hourly demand calibration, and an accurate weather data file is gathered. Next, sensitivity analysis is conducted to identify the key fixed parameters for the calibration process. Following the calibration of these fixed parameters, the final level involves the calibration of dynamic variables to achieve a robust hourly agreement between simulated and measured data. The developed strategy is implemented in a multi-purpose building located in the Aalto University campus in Finland. The building is simulated as a simplified five-zone model developed in the whole-building simulation software IDA-ICE, including various educational sections, workshops, a shopping center, and a metro station. Sensors and meters are used to measure the hourly indoor air temperature by zone, whereas the calibration aims at minimizing the difference between measured and simulated heating and cooling energy demands. In conclusion, the proposed calibration strategy appears to be successful in facilitating hourly synchronization between simplified simulation models and multi-purpose buildings.

Weather records from recent years performed better than analogue years when merging with real-time weather measurements for dynamic within-season predictions of rainfed maize yield

Within-season crop yield prediction with a dynamic crop model can provide valuable references for field management practices and regional food security. However, weather ensembles containing the unknown future weather conditions occurring after prediction dates are essential for such predictions using crop models. Two strategies were established for selecting analogue weather years as the target growing season based on a five-year maize experiment conducted at eight sites in the Loess Plateau of China. The first strategy tried weather data from different lengths of years ahead the planting year. The second strategy used the k-nearest neighbor (k-NN) algorithm to select analogue weather according to different combinations of weather variables with daily or accumulative values. The results showed that satisfactory predictions could be obtained after maize tasseling (about 50 d prior to maturity). The mean absolute relative error (ARE) and coefficient of variation (CV) of the daily yield predictions after tasseling were 6.6% and 5.7%, respectively, in 2010 at the Yulin site. In the leading-year strategy, the most reliable predictions were obtained by the weather data from the 10 years ahead of planting, with an overall average ARE of 11.7%. In the k-NN strategy, the most reliable predictions were obtained by using the analogue weather selected with only accumulative precipitation, with an overall average ARE of 11.5%. Additionally, both of the two optimal strategies improved the original predictions in most cases. However, the k-NN strategy was more likely to generate worse predictions in the early part of the growing season. Generally, it was more convenient to use the weather data of 10 leading years before the planting year to represent the unknown weather data after the prediction dates. This strategy provided reliable prediction accuracy without complex programming and requirement for long-term weather records.

Evaluating model-based strategies for in-season nitrogen management of maize using weather data fusion

One challenge in precision nitrogen (N) management is the uncertainty in future weather conditions at the time of decision-making. Crop growth models require a full season of weather data to run yield simulation, and the unknown weather data may be forecasted or substituted by historical data. The objectives of this study were to (1) develop a model-based in-season N recommendation strategy for maize (Zea mays L.) using weather data fusion; and (2) evaluate this strategy in comparison with farmers’ N rate and regional optimal N rate in Northeast China. The CERES-Maize model was calibrated using data collected from field experiments conducted in 2015 and 2016, and validated using data from 2017. At two N decision dates - planting stage and V8 stage, the calibrated CERES-Maize model was used to predict grain yield and plant N uptake by fusing current and historical weather data. Using this approach, the model simulated grain yield and plant N uptake well (R2 = 0.85–0.89). Then, in-season economic optimal N rate (EONR) was determined according to responses of simulated marginal return (based on predicted grain yield) to N rate at planting and V8 stages. About 83% of predicted EONR fell within 20% of measured values. Applying the model-based in-season EONR had the potential to increase marginal return by 120–183 $ ha−1 and 0–83 $ ha−1 and N use efficiency by 8–71% and 1–38% without affecting grain yield over farmers’ N rate and regional optimal N rate, respectively. It is concluded that the CERES-Maize model is a valuable tool for simulating yield responses to N under different planting densities, soil types and weather conditions. The model-based in-season N recommendation strategy with weather data fusion can improve maize N use efficiency compared with current farmer practice and regional optimal management practice.

Optimal travel strategy model study

In this paper, we propose an appropriate algorithm to determine the best strategy of an individual in the Through the Desert game, and give the generally applicable models for single-player and multi-player games under known and unknown weather conditions respectively. We integrate ant colony algorithm, branch-and-bound method and game theory to build and solve the optimization model.

Weather Contracts: Capturing a sense of weather for place-based adaptation to climate change

Place attachment, the emotional bonds that people form to significant places, influences adaptation to climate change. Within this context, weather is garnering greater attention for its dynamic, mediating role, yet its political and cultural significance remains under-researched. Here we draw on Serres and McCarren’s (1992) idea of the Natural Contract and Vannini et al.’s (2012) contributions on weathering to integrate contractarianism with a deep account of people’s relationship with weather in place. We analyse attitudes and adaptation to climate change in communities of the geographically-remote and climate-vulnerable Outer Hebrides in Scotland, using video-elicitation to generate data on significant places. Our results show that changing, difficult and unpredictable weather binds people to place and influences how they think about themselves, their place and adaptation in dynamic ways. Through this connection, we demonstrate that people knowingly enter into what we term Weather Contracts and that accepting the volatility of the weather allows people to react positively to changes brought with climate. Finally, we show that the common ideology of a community living with weather generates wider discourses around independence and resisting modernisation that we term a weathered ideology. Thus, climate change is not always a destabilising force. For those who are accustomed to changing weather it can be a dimension of place around which people can organise. Uncertainty and anxiety about the future of the climate is caused more by a lack of control over adaptation processes than by a fear of unknown weather. This has implications for people living at the margins across the globe, where unpredictable weather is a part of local identities, but influence on adaptation policy making is low.

Stochastic weather generator for the design and reliability evaluation of desalination systems with Renewable Energy Sources

Abstract The operation of Renewable Energy Sources (RES) systems is highly affected by the continuously changing meteorological conditions and the design of a RES system has to be robust to the unknown weather conditions that it will encounter during its lifetime. In this paper, the use of Stochastic Weather Generators (SWGENs) is introduced for the optimal design and reliability evaluation of hybrid Photovoltaic/Wind-Generator systems providing energy to desalination plants. A SWGEN is proposed, which is based on parametric Markov-Switching Auto-Regressive (MSAR) models and is capable to simulate realistic hourly multivariate time series of solar irradiance, temperature and wind speed of the target installation site. Numerical results are presented, demonstrating that: (i) SWGENs enable to evaluate the reliability of RES-based desalination plants during their operation over a 20 years lifetime period and (ii) using an appropriate time series simulated with a SWGEN as input to the design optimization process results in a RES-based desalination plant configuration with higher reliability compared to the configurations derived when the other types of meteorological datasets are used as input to the design optimization process.

How good are the models available for estimating sugar content in sugarcane?

Abstract While sugarcane growth models could assist farmers in harvesting fields as close as possible to peak maturity, few of them are available to the industry. Even in those that are available, either there is room for improvement in sugar content estimation, or they were not properly assessed as to how they would perform given weather variability. In this work, we show that when developed with a dataset comprised of typical weather patterns, the outputs of empirical models that have been recently developed are qualitatively analogous to those of a process-based model and have smaller errors. However, when the training data is not representative, the same doesn’t happen and they are not consistent with known responses from sugarcane and with the output of mechanistic models. We used data from three years of harvests of a sugarcane mill to develop and evaluate the performance of machine learning models, as well as to evaluate an empirical model recently developed and DSSAT/Canegro. All models’ performances were evaluated in each of the three years separately, as well as through sensitivity analysis, to observe the effects of unknown weather in the estimates obtained by the model. This evaluation suggests that while machine learning techniques applied to industry data may be a promising tool for decision-makers, by themselves they are not capable of capturing all the effects that influence sucrose accumulation in sugarcane stalks.

Global evaluation of a semiempirical model for yield anomalies and application to within-season yield forecasting.

Quantifying the influence of weather on yield variability is decisive for agricultural management under current and future climate anomalies. We extended an existing semiempirical modeling scheme that allows for such quantification. Yield anomalies, measured as interannual differences, were modeled for maize, soybeans, and wheat in the United States and 32 other main producer countries. We used two yield data sets, one derived from reported yields and the other from a global yield data set deduced from remote sensing. We assessed the capacity of the model to forecast yields within the growing season. In the United States, our model can explain at least two-thirds (63%-81%) of observed yield anomalies. Its out-of-sample performance (34%-55%) suggests a robust yield projection capacity when applied to unknown weather. Out-of-sample performance is lower when using remote sensing-derived yield data. The share of weather-driven yield fluctuation varies spatially, and estimated coefficients agree with expectations. Globally, the explained variance in yield anomalies based on the remote sensing data set is similar to the United States (71%-84%). But the out-of-sample performance is lower (15%-42%). The performance discrepancy is likely due to shortcomings of the remote sensing yield data as it diminishes when using reported yield anomalies instead. Our model allows for robust forecasting of yields up to 2months before harvest for several main producer countries. An additional experiment suggests moderate yield losses under mean warming, assuming no major changes in temperature extremes. We conclude that our model can detect weather influences on yield anomalies and project yields with unknown weather. It requires only monthly input data and has a low computational demand. Its within-season yield forecasting capacity provides a basis for practical applications like local adaptation planning. Our study underlines high-quality yield monitoring and statistics as critical prerequisites to guide adaptation under climate change.

Distributions-oriented wind forecast verification by a hidden Markov model for multivariate circular–linear data

Winds from the North–West quadrant and lack of precipitation are known to lead to an increase of PM10 concentrations over a residential neighborhood in the city of Taranto (Italy). In 2012 the local government prescribed a reduction of industrial emissions by 10% every time such meteorological conditions are forecasted 72 h in advance. Wind forecasting is addressed using the Weather Research and Forecasting (WRF) atmospheric simulation system by the Regional Environmental Protection Agency. In the context of distributions-oriented forecast verification, we propose a comprehensive model-based inferential approach to investigate the ability of the WRF system to forecast the local wind speed and direction allowing different performances for unknown weather regimes. Ground-observed and WRF-forecasted wind speed and direction at a relevant location are jointly modeled as a 4-dimensional time series with an unknown finite number of states characterized by homogeneous distributional behavior. The proposed model relies on a mixture of joint projected and skew normal distributions with time-dependent states, where the temporal evolution of the state membership follows a first order Markov process. Parameter estimates, including the number of states, are obtained by a Bayesian MCMC-based method. Results provide useful insights on the performance of WRF forecasts in relation to different combinations of wind speed and direction.

The effect of atmosphere temperature on out-of-hospital cardiac arrest outcomes

BackgroundIt is unclear whether the atmosphere temperature is associated with outcomes after out-of-hospital cardiac arrest (OHCA). MethodsThis is a nationwide observational study using the national OHCA registry merged with a geographical and weather database. Adult patients with a cardiac etiology that occurred from 2006 to 2013 were included, excluding patients with unknown outcome or unknown weather information. The main exposure was the hourly measured temperature matched to the OHCA event time. The covariates were age, gender, metropolis, place, and weather factors (wind speed and humidity). The primary outcome was good cerebral performance scale (CPC) 1 or 2. Patients were classified with three temperature groups by quartile range: Cold (<4°C), Intermediate (4–21°C), and Hot (≥22°C). We tested the associations between the atmosphere temperature (by 1°C and by temperature group) and outcomes using multivariable logistic regression analysis. ResultsOf 17,3051 OHCAs, a total of 115,578 cases were matched to weather database. A total of 78,717 OHCAs were analyzed. The proportion of good CPC 1 or 2 was 1.7% in the Cold group, 1.8% in the Intermediate group, and 2.3% in the Hot group. As the temperature at the event of OHCA increased by 1°C, AORs (95% CIs) were 1.006 (1.002–1.009) for good CPC. The AORs (95% CIs) for good CPC in Cold and Hot group comparing with Intermediate group for good CPC were 0.964 (0.845–1.100) and 1.246 (1.096–1.416), respectively. ConclusionThe temperature at the time of the OHCA event was associated with outcomes after OHCA in a nationwide observational study in Korea

Rainfall effect on single-vehicle crash severities using polychotomous response models

As part of the Wisconsin road weather safety initiative, the objective of this study is to assess the effects of rainfall on the severity of single-vehicle crashes on Wisconsin interstate highways utilizing polychotomous response models. Weather-related factors considered in this study include estimated rainfall intensity for 15 min prior to a crash occurrence, water film depth, temperature, wind speed/direction, stopping sight distance and deficiency of car-following distance at the crash moment. For locations with unknown weather information, data were interpolated using the inverse squared distance method. Non-weather factors such as road geometrics, traffic conditions, collision types, vehicle types, and driver and temporal attributes were also considered. Two types of polychotomous response models were compared: ordinal logistic and sequential logistic regressions. The sequential logistic regression was tested with forward and backward formats. Comparative models were also developed for single vehicle crash severity during clear weather. In conclusion, the backward sequential logistic regression model produced the best results for predicting crash severities in rainy weather where rainfall intensity, wind speed, roadway terrain, driver's gender, and safety belt were found to be statistically significant. Our study also found that the seasonal factor was significant in clear weather. The seasonal factor is a predictor suggesting that inclement weather may affect crash severity. These findings can be used to determine the probabilities of single vehicle crash severity in rainy weather and provide quantitative support on improving road weather safety via weather warning systems, highway facility improvements, and speed limit management.

PREPROCESSING QUANTIFIED CONSTRAINT SATISFACTION PROBLEMS WITH VALUE REORDERING AND DIRECTIONAL ARC AND PATH CONSISTENCY

The Quantified Constraint Satisfaction Problem (QCSP) is an extension of the CSP that can be used to model combinatorial problems containing contingency or uncertainty. It allows for universally quantified variables that can model uncertain actions and events, such as the unknown weather for a future party, or an opponent's next move in a game. Although interest in QCSPs is increasing in recent years, the development of techniques for handling QCSPs is still at an early stage. For example, although it is well known that local consistencies are of primary importance in CSPs, only arc consistency has been extended to quantified problems. In this paper we contribute towards the development of solution methods for QCSPs in two ways. First, by extending directional arc and path consistency, two popular local consistencies in constraint satisfaction, to the quantified case and proposing an algorithm that achieves these consistencies. Second, by showing how value ordering heuristics can be utilized to speed up computation in QCSPs. We study the impact of preprocessing QCSPs with value reordering and directional quantified arc and path consistency by running experiments on randomly generated problems. Results show that our preprocessing methods can significantly speed up the QCSP solving process, especially on hard instances from the phase transition region.

Solving quantified constraint satisfaction problems

We make a number of contributions to the study of the Quantified Constraint Satisfaction Problem (QCSP). The QCSP is an extension of the constraint satisfaction problem that can be used to model combinatorial problems containing contingency or uncertainty. It allows for universally quantified variables that can model uncertain actions and events, such as the unknown weather for a future party, or an opponent's next move in a game. In this paper we report significant contributions to two very different methods for solving QCSPs. The first approach is to implement special purpose algorithms for QCSPs; and the second is to encode QCSPs as Quantified Boolean Formulas and then use specialized QBF solvers. The discovery of particularly effective encodings influenced the design of more effective algorithms: by analyzing the properties of these encodings, we identify the features in QBF solvers responsible for their efficiency. This enables us to devise analogues of these features in QCSPs, and implement them in special purpose algorithms, yielding an effective special purpose solver, QCSP-Solve. Experiments show that this solver and a highly optimized QBF encoding are several orders of magnitude more efficient than the initially developed algorithms. A final, but significant, contribution is the identification of flaws in simple methods of generating random QCSP instances, and a means of generating instances which are not known to be flawed.

Vision and the Atmosphere

Current vision systems are designed to perform in clear weather. Needless to say, in any outdoor application, there is no escape from “bad” weather. Ultimately, computer vision systems must include mechanisms that enable them to function (even if somewhat less reliably) in the presence of haze, fog, rain, hail and snow. We begin by studying the visual manifestations of different weather conditions. For this, we draw on what is already known about atmospheric optics, and identify effects caused by bad weather that can be turned to our advantage. Since the atmosphere modulates the information carried from a scene point to the observer, it can be viewed as a mechanism of visual information coding. We exploit two fundamental scattering models and develop methods for recovering pertinent scene properties, such as three-dimensional structure, from one or two images taken under poor weather conditions. Next, we model the chromatic effects of the atmospheric scattering and verify it for fog and haze. Based on this chromatic model we derive several geometric constraints on scene color changes caused by varying atmospheric conditions. Finally, using these constraints we develop algorithms for computing fog or haze color, depth segmentation, extracting three-dimensional structure, and recovering “clear day” scene colors, from two or more images taken under different but unknown weather conditions.

SIGNIFICANCE OF B-CELL FLOW CYTOMETRIC CROSSMATCHING IN TACROLIMUS TREATED RENAL ALLOGRAFT RECIPIENTS

308 Aim: A pretransplant positive (+) flow cytometric crossmatch (FCXM) is associated with early allograft failure in cyclosporine (CSA) treated kidney transplant (KTx) recipients. Enhanced immunosuppression (IS) may prevent these early failures. We therefore analyzed our initial experience with tacrolimus (FK) in FCXM (+) cadaveric KTx and compared with CSA treated recipients. Methods: 167 consecutive cadaveric KTx performed between 1995 and 1998 were studied. All pts had a negative (−) Amos one-wash T-cell XM. IS included steroids, ATGAM, and CSA (between 1995-1997), or FK (between 1997-1998). The pts were divided into 4 groups by FCXM status and IS regimen. Actuarial graft survivals were determined by the Kaplan-Meier method and curves were compared by log rank with a p value of <0.05 considered significant. Results: The groups were comparable with respect to recipient age, sex, race, PRA, mean HLA mismatches, retransplant status, and donor variables. Follow up was 20-46 months for CSA recipients and 1-20 months for FK. (Table) Graft survival in CSA FCXM (+) pts was significantly diminished compared to CSA FCXM (−) recipients. FK treated patients experienced equivalent success rates in FCXM (+) and (−) groups. Although follow-up is limited to 20 months in FK treated patients, early failures associated with (+) FCXM were reduced with FK treatment. It is unknown weather this will remain true over the long term.TableConclusion: We conclude that a (+) B cell FCXM has no detrimental impact on early outcome in FK treated cadaveric renal allograft recipients. Selecting recipients based on FCXM denies many patients potentially successful KTx. An intensified IS regimen may achieve satisfactory results despite the presence of low level donor-reactive antibodies detectable by FCXM.