scholarly journals Using the Residual Network Module to Correct the Sub-Seasonal High Temperature Forecast

2022 ◽  
Vol 9 ◽  
Author(s):  
Wei Jin ◽  
Wei Zhang ◽  
Jie Hu ◽  
Bin Weng ◽  
Tianqiang Huang ◽  
...  
Keyword(s):  
High Temperature ◽  
Absolute Error ◽  
Residual Network ◽  
Residual Structure ◽  
Convolutional Networks ◽  
Temperature Forecast ◽  
Anomaly Correlation Coefficient ◽  
Extreme High Temperature ◽  
Temperature Forecasting ◽  
Forecast Models

The high temperature forecast of the sub-season is a severe challenge. Currently, the residual structure has achieved good results in the field of computer vision attributed to the excellent feature extraction ability. However, it has not been introduced in the domain of sub-seasonal forecasting. Here, we develop multi-module daily deterministic and probabilistic forecast models by the residual structure and finally establish a complete set of sub-seasonal high temperature forecasting system in the eastern part of China. The experimental results indicate that our method is effective and outperforms the European hindcast results in all aspects: absolute error, anomaly correlation coefficient, and other indicators are optimized by 8–50%, and the equitable threat score is improved by up to 400%. We conclude that the residual network has a sharper insight into the high temperature in sub-seasonal high temperature forecasting compared to traditional methods and convolutional networks, thus enabling more effective early warnings of extreme high temperature weather.

scholarly journals Transformer for Sub-Seasonal Extreme High Temperature Probabilistic Forecasting Over Eastern China

2022 ◽  
Author(s):  
Wei Jin ◽  
Wei Zhang ◽  
Jie Hu ◽  
Jiazhen Chen ◽  
Bin Weng ◽  
...  
Keyword(s):  
Deep Learning ◽  
High Temperature ◽  
Language Processing ◽  
Spatial Information ◽  
Eastern China ◽  
Extended Period ◽  
Absolute Error ◽  
Probabilistic Forecasting ◽  
Prediction Probability ◽  
Extreme High Temperature

Abstract Sub-seasonal high temperature forecasting is significant for early warning of extreme heat weather. Currently, deep learning methods, especially Transformer, have been successfully applied to the meteorological field. Relying on the excellent global feature extraction capability in natural language processing, Transformer may be useful to improve the ability in extended periods. To explore this, we introduce the Transformer and propose a Transformer-based model, called Transformer to High Temperature (T2T). In the details of the model, we successively discuss the use of the Transformer and the position encoding in T2T to continuously optimize the model structure in an experimental manner. In the dataset, the multi-version data fusion method is proposed to further improve the prediction of the model with reasonable expansion of the dataset. The performance of well-desinged model (T2T) is verified against the European Centre for Medium-Range Weather Forecasts (ECMWF) and Multi-Layer Perceptron (MLP) at each grid of the 100.5°E to 138°E, 21°N to 54°N domain for the April to October of 2016-2019. For case study initiated from 2 June 2018, the results indicated that T2T is significantly better than ECMWF and MLP, with smaller absolute error and more reliable probabilistic forecast for the extreme high event happened during the third week. Over all, the deterministic forecast of T2T is superior to MLP and ECMWF due to ability of utilize spatial information of grids. T2T also provided a better calibrated probability of high temperature and a sharper prediction probability density function than MLP and ECMWF. All in all, T2T can meet the operational requirements for extended period forecasting of extreme high temperature. Furthermore, our research can provide experience on the development of deep learning in this field and achieve the continuous progress of seamless forecasting systems.

Host choice, settling and folding leaf behaviors of the larval rice leaf folder under heat stress

2016 ◽  
Vol 106 (6) ◽  
pp. 809-817 ◽  
Author(s):  
M.A. Bodlah ◽  
A.-X. Zhu ◽  
X.-D. Liu
Keyword(s):  
Heat Stress ◽  
High Temperature ◽  
Host Choice ◽  
Cnaphalocrocis Medinalis ◽  
Behavioral Traits ◽  
The Third ◽  
Rice Leaf ◽  
Rice Leaf Folder ◽  
Extreme High Temperature ◽  
Third Instar Larvae

AbstractExtreme high-temperature events are the key factor to determine population dynamics of the rice leaf folder,Cnaphalocrocis medinalis(Guenée), in summer. Although we know that adult of this insect can migrate to avoid heat stress, the behavioral response of larva to high temperature is still unclear. Therefore, impacts of high temperature on behavioral traits ofC. medinalisincluding host choice, settling and folding leaf were observed. The results revealed that these behavioral traits were clearly influenced by high temperature. The larvae preferred maize leaves rather than rice and wheat at normal temperature of 27°C, but larvae experienced a higher temperature of 37 or 40°C for 4 h preferred rice leaves rather than maize and wheat. Capacity of young larvae to find host leaves or settle on the upper surface of leaves significantly reduced when they were treated by high temperature. High temperature of 40°C reduced the leaf-folding capacity of the third instar larvae, but no effects were observed on the fourth and fifth instar larvae. Short-term heat acclimation could not improve the capacity of the third instar larvae to make leaf fold under 40°C.

EDPANs: Enhanced Dual Path Attention Networks for Single Image Super-Resolution

2021 ◽  
pp. 2150300
Author(s):  
Guoan Cheng ◽  
Ai Matsune ◽  
Huaijuan Zang ◽  
Toru Kurihara ◽  
Shu Zhan
Keyword(s):  
State Of The Art ◽  
Super Resolution ◽  
Single Image ◽  
Residual Network ◽  
Dense Structure ◽  
Attention Networks ◽  
Residual Structure ◽  
Quantitative Results ◽  
Image Super Resolution ◽  
Single Image Super Resolution

In this paper, we propose an enhanced dual path attention network (EDPAN) for image super-resolution. ResNet is good at implicitly reusing extracted features, DenseNet is good at exploring new features. Dual Path Network (DPN) combines ResNets and DenseNet to create a more accurate architecture than the straightforward one. We experimentally show that the residual network performs best when each block consists of two convolutions, and the dense network performs best when each micro-block consists of one convolution. Following these ideas, our EDPAN exploits the advantages of the residual structure and the dense structure. Besides, to deploy the computations for features more effectively, we introduce the attention mechanism into our EDPAN. Moreover, to relieve the parameters burden, we also utilize recursive learning to propose a lightweight model. In the experiments, we demonstrate the effectiveness and robustness of our proposed EDPAN on different degradation situations. The quantitative results and visualization comparison can sufficiently indicate that our EDPAN achieves favorable performance over the state-of-the-art frameworks.

Different responses of scleractinian coral Acropora pruinosa from Weizhou Island during extreme high temperature events

Coral Reefs ◽  
2021 ◽  
Author(s):  
Xiaopeng Yu ◽  
Kefu Yu ◽  
Biao Chen ◽  
Zhiheng Liao ◽  
Jiayuan Liang ◽  
...  
Keyword(s):  
High Temperature ◽  
Scleractinian Coral ◽  
Extreme High Temperature

scholarly journals Seeking Consensus: A New Approach

2010 ◽  
Vol 138 (12) ◽  
pp. 4402-4415 ◽  
Author(s):  
Paul J. Roebber
Keyword(s):  
Minimum Temperature ◽  
Forecast Accuracy ◽  
Cost Savings ◽  
Absolute Error ◽  
Electricity Production ◽  
Forecast Errors ◽  
Consensus Forecasts ◽  
Temperature Forecast ◽  
Model Output Statistics ◽  
The Individual

Abstract Simulated evolution is used to generate consensus forecasts of next-day minimum temperature for a site in Ohio. The evolved forecast algorithm logic is interpretable in terms of physics that might be accounted for by experienced forecasters, but the logic of the individual algorithms that form the consensus is unique. As a result, evolved program consensus forecasts produce substantial increases in forecast accuracy relative to forecast benchmarks such as model output statistics (MOS) and those from the National Weather Service (NWS). The best consensus produces a mean absolute error (MAE) of 2.98°F on an independent test dataset, representing a 27% improvement relative to MOS. These results translate to potential annual cost savings for electricity production in the state of Ohio of the order of $2 million relative to the NWS forecasts. Perfect forecasts provide nearly $6 million in additional annual electricity production cost savings relative to the evolved program consensus. The frequency of outlier events (forecast busts) falls from 24% using NWS to 16% using the evolved program consensus. Information on when busts are most likely can be provided through a logistic regression equation with two variables: forecast wind speed and the deviation of the NWS minimum temperature forecast from persistence. A forecast of a bust is 4 times more likely to be correct than wrong, suggesting some utility in anticipating the most egregious forecast errors. Discussion concerning the probabilistic applications of evolved programs, the application of this technique to other forecast problems, and the relevance of these findings to the future role of human forecasting is provided.

scholarly journals Comparison of Atmospheric Circulation Anomalies between Dry and Wet Extreme High-Temperature Days in the Middle and Lower Reaches of the Yellow River

Atmosphere ◽  
2021 ◽  
Vol 12 (10) ◽  
pp. 1265
Author(s):  
Hangcheng Ge ◽  
Gang Zeng ◽  
Vedaste Iyakaremye ◽  
Xiaoye Yang ◽  
Zongming Wang
Keyword(s):  
High Temperature ◽  
Atmospheric Circulation ◽  
Yellow River ◽  
Wave Train ◽  
The Yellow River ◽  
South Asian High ◽  
Opposite Pattern ◽  
Extreme High Temperature ◽  
Circulation Anomalies ◽  
Atmospheric Circulation Anomalies

Many previous studies have reported that atmospheric circulation anomalies are generally the direct cause of extreme high-temperature (EHT). However, the atmospheric circulation anomalies of EHT days with different humidity and the differences between them are less often discussed, while humidity plays an important role in how people feel in a high-temperature environment. Therefore, this study uses 1961–2016 CN05.1 daily observational data and NCEP/NCAR reanalysis data to classify summer EHT days in China into dry and wet. Furthermore, we investigate the atmospheric circulation anomalies associated with the dry and wet EHT days in the middle and lower reaches of the Yellow River (MLRYR). The results reveal that dry EHT days are likely to be caused by adiabatic heating from anomalous subsidence, while wet EHT days are more likely caused by the low-latitude water vapor and heat anomalies brought by the Western Pacific Subtropical High (WPSH). This may be due to a remarkable westward/southward/narrowed extension of the Continental High (CH)/WPSH/South Asian High (SAH) accompanied by an occurrence of dry EHT day. The opposite pattern is observed for wet EHT days. Moreover, a wave train like the Silk Road pattern from the midlatitudes could affect the dry EHT days, while wet EHT days are more likely to be affected by a wave train from high latitudes. Knowing the specific characteristics of dry and wet EHT days and their associated atmospheric circulations could offer new insights into disaster risk prevention and reduction.

scholarly journals Extreme High‐Temperature Events Over East Asia in 1.5°C and 2°C Warmer Futures: Analysis of NCAR CESM Low‐Warming Experiments

2018 ◽  
Vol 45 (3) ◽  
pp. 1541-1550 ◽  
Author(s):  
Donghuan Li ◽  
Tianjun Zhou ◽  
Liwei Zou ◽  
Wenxia Zhang ◽  
Lixia Zhang
Keyword(s):  
High Temperature ◽  
East Asia ◽  
Extreme High Temperature

The Met Office operational global ocean forecast system FOAM-ORCA12

2021 ◽  
Author(s):  
Ana Barbosa Aguiar ◽  
Jennifer Waters ◽  
Martin Price ◽  
Gordon Inverarity ◽  
Christine Pequignet ◽  
...  
Keyword(s):  
Computational Cost ◽  
Absolute Error ◽  
Global Ocean ◽  
Background Error ◽  
Verification Methods ◽  
Climate Simulations ◽  
Forecast Models ◽  
The Mean ◽  
Lower Continuous ◽  
Better Than

<div> <p>The importance of oceans for atmospheric forecasts as well as climate simulations is being increasingly recognised with the advent of coupled ocean / atmosphere forecast models. Having comparable resolutions in both domains maximises the benefits for a given computational cost. The Met Office has recently upgraded its operational global ocean-only model from an eddy permitting 1/4 degree tripolar grid (ORCA025) to the eddy resolving 1/12 degree ORCA12 configuration while retaining 1/4 degree data assimilation. </p> </div><div> <p>We will present a description of the ocean-only ORCA12 system, FOAM-ORCA12, alongside some initial results. Qualitatively, FOAM-ORCA12 seems to represent better (than FOAM-ORCA025) the details of mesoscale features in SST and surface currents. Overall, traditional statistical results suggest that the new FOAM-ORCA12 system performs similarly or slightly worse than the pre-existing FOAM-ORCA025. However, it is known that comparisons of models running at different resolutions suffer from a double penalty effect, whereby higher-resolution models are penalised more than lower-resolution models for features that are offset in time and space. Neighbourhood verification methods seek to make a fairer comparison using a common spatial scale for both models and it can be seen that, as neighbourhood sizes increase, ORCA12 consistently has lower continuous ranked probability scores (CRPS) than ORCA025. CRPS measures the accuracy of the pseudo-ensemble created by the neighbourhood method and generalises the mean absolute error measure for deterministic forecasts. </p> </div><div> <p>The focus over the next year will be on diagnosing the performance of both the model and assimilation. A planned development that is expected to enhance the system is the update of the background-error covariances used for data assimilation. </p> </div>

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