Anomaly Coefficient Research Articles

Seasonal climate forecasts show skill in predicting winter chill for specialty crops in California

Many fruits and nuts crops in California require sufficient winter chill to break dormancy, and insufficient chill can harm fruit quantity and quality. Early information on winter chill forecast can help growers prepare for a low chill year. Here we evaluate use of dynamic climate models for chill accumulation forecast in California. Using temperature forecasts from seasonal prediction systems, we found that the multimodel forecasts can predict chill. This is evident from the anomaly correlation coefficients exceeding 0.5 between the model-predicted and reference chill values for most California regions. The forecasts correctly identified chill categories in over 50% instances in more than 40% of the Central Valley and southern parts of California. The forecasts also demonstrated skill in capturing the interannual variability of chill, especially during years with substantial decrease in chill. Additionally, the seasonal forecast can provide potentially useful crop specific chill sufficiency prediction. However, forecasts beyond a one-month lead time showed reduced forecast skills.

Integration of an autoencoder and background suppression for hyperspectral anomaly detection

ABSTRACT The key to hyperspectral image (HSI) anomaly detection is effectively distinguishing anomalies from the background. Various detectors, such as low-rank sparse representation and background estimation, have been proposed. However, they usually exhibit poor performance due to complex backgrounds and similar anomalies and backgrounds. Considering that most of the pixels in HSI are background, the autoencoder can emphatically and automatically extract and reconstruct background features. And background suppression can further enhance the spectral differentiation of the anomalies and backgrounds. Therefore, we propose a new anomaly detector by integrating autoencoder and background suppression. First, the anomaly coefficient of HSI data is extracted via the local characteristics. The HSI data is fed into the 2Dimensional (2D) convolution autoencoder to reconstruct the background. Since the reconstruction background contains incorrect anomalies, the anomaly coefficient isused to suppress the reconstruction image. Then, the detection map is obtained with the Mahalanobis distance. The experiment is performed in different HSI datasets. The results illustrate that the proposed method is effective and outperforms other advanced methods in terms of detection performance.

Light-ray sum rules and the c-anomaly

In a four-dimensional quantum field theory that flows between two fixed points under the renormalization group, the change in the conformal anomaly ∆a has been related to the average null energy. We extend this result to derive a sum rule for the other anomaly coefficient, ∆c, in terms of the stress tensor three-point function. While the sum rule for ∆a is an expectation value of the averaged null energy operator, and therefore positive, the result for ∆c involves the off-diagonal matrix elements, so it does not have a fixed sign.

Assessment of seasonal forecasting potential for springtime Asian dust in South Korea using the KMA global seasonal forecasting system

This study aims to assess the potential of seasonal forecasting for springtime Asian dust days in South Korea using the Korea Meteorological Administration's Global Seasonal Forecasting System version 6 (KMA GloSea6). The system demonstrated similar predictive performance for springtime Asian dust days compared to GloSea5-ADAM, which incorporates the Asian Dust and Aerosol Model's emission algorithm into GloSea5. While KMA GloSea6 exhibited strong wind, cold, and moist biases in the Asian dust source region during the spring of the hindcast period, similar to GloSea5-ADAM, it showed an improved spatial distribution of ACC. The anomaly correlation coefficient for the annual variability of Asian dust occurrence frequency anomalies in the dust source region was also similar, with values of 0.41 for KMA GloSea6 and 0.43 for GloSea5-ADAM. Furthermore, in evaluating the seasonal forecasting of springtime Asian dust days using hindcast datasets, KMA GloSea6 accurately predicted 12 out of 24 instances, resulting in a forecast accuracy of 0.5, consistent with GloSea5-ADAM. When applying the criteria to spring 2022 and 2023 forecast data not used in the derivation, the predicted Asian dust days of 6.14 days in spring 2022 led to a “Near normal” prediction, differing from the observed “Below normal.” However, the spatial distribution showed “Below normal” conditions, aligning with observations in some areas. In spring 2023, the predicted 9.78 Asian dust days led to an “Above normal” prediction, consistent with observed conditions. These findings will contribute to advancing seasonal Asian dust forecasting in South Korea.

Sub-seasonal prediction skill: is the mean state a good model evaluation metric?

Evaluating forecast models encompasses assessing their ability to accurately depict observed climate states and predict future climate variables. Various evaluation methods, from computationally efficient measures like the anomaly correlation coefficient to more intricate approaches, have been formulated. While simpler methods provide limited information, climatology, due to its simplicity and immediate linkage to model performance, is a commonly utilized primary evaluation metric. In this study focusing on temperature and precipitation, we propose a novel metric based on the model’s mean state, integrating both climatology and the seasonal cycle for a more accurate assessment of the relationship between mean state performance and prediction skill on weather and sub-seasonal time scales compared to relying solely on climatology. This integrated metric reveals a robust correlation between temperature and precipitation across diverse geographical locations, with a more pronounced effect in tropical areas when considering the seasonal cycle. Additionally, we find that temperature exhibits higher prediction skill compared to precipitation. The discovered relationship serves as a potential early indicator for predicting the efficacy of Seasonal to Sub-seasonal (S2S) models and offers valuable insights for model development, emphasizing the significance of this integrated metric in enhancing S2S model performance and advancing climate prediction capabilities.

Higher-derivative corrections to flavoured BPS black hole thermodynamics and holography

A Cardy-like regime of the four-dimensional superconformal index has been shown to be governed by ’t Hooft anomalies and to single out a large-N saddle carrying the Bekenstein-Hawking entropy of dual supersymmetric black holes in AdS5. For the universal index where no flavour fugacities are turned on, this correspondence has been improved by matching the first subleading corrections to the saddle-point action with the four-derivative corrections to the black hole action in minimal gauged supergravity, as well as the respective corrected entropies. Here, we extend this match by including flavour symmetries. We consider five-dimensional gauged supergravity with vector multiplet and four-derivative couplings, and provide an effective theory reproducing the ’t Hooft anomalies of the R- and flavour symmetries of generic holographic superconformal field theories at next-to-leading order in the large-N expansion. Then we focus on a specific model dual to ℂ3/ℤν quiver gauge theories, where the ’t Hooft anomaly coefficients receive simple but sufficiently generic corrections. In this model, we evaluate the four-derivative corrections to the on-shell action of the supersymmetric multi-charge black hole, showing agreement with the flavoured Cardy-like formula from the index. We give a prediction for the corrected entropy of the supersymmetric black hole and discuss the general validity of our results. Taking the limit of infinite AdS5 radius, we also obtain four-derivative corrections to the action and entropy of supersymmetric asymptotically flat black holes.

A Study of Precipitation Forecasting for the Pre-Summer Rainy Season in South China Based on a Back-Propagation Neural Network

In South China, the large quantity of rainfall in the pre-summer rainy season can easily lead to natural disasters, which emphasizes the importance of improving the accuracy of precipitation forecasting during this period for the social and economic development of the region. In this paper, the back-propagation neural network (BPNN) is used to establish the model for precipitation forecasting. Three schemes are applied to improve the model performance: (1) predictors are selected based on individual meteorological stations within the region rather than the region as a whole; (2) the triangular irregular network (TIN) is proposed to preprocess the observed precipitation data for input of the BPNN model, while simulated/forecast precipitation is the expected output; and (3) a genetic algorithm is used for the hyperparameter optimization of the BPNN. The first scheme reduces the mean absolute percentage error (MAPE) and the root mean square error (RMSE) of the simulation by roughly 5% and more than 15 mm; the second reduces the MAPE and RMSE by more than 15% and 15 mm, respectively, while the third improves the simulation inapparently. Obviously, the second scheme raises the upper limit of the model simulation capability greatly by preprocessing the precipitation data. During the training and validation periods, the MAPE of the improved model can be controlled at approximately 35%. For precipitation hindcasting in the test period, the anomaly rate is less than 50% in only one season, and the highest is 64.5%. According to the anomaly correlation coefficient and Ps score of the hindcast precipitation, the improved model performance is slightly better than the FGOALS-f2 model. Although global climate change makes the predictors more variable, the trend of simulation is almost identical to that of the observed values over the whole period, suggesting that the model is able to capture the general characteristics of climate change.

Streamlining Ocean Dynamics Modeling with Fourier Neural Operators: A Multiobjective Hyperparameter and Architecture Optimization Approach

Training an effective deep learning model to learn ocean processes involves careful choices of various hyperparameters. We leverage DeepHyper’s advanced search algorithms for multiobjective optimization, streamlining the development of neural networks tailored for ocean modeling. The focus is on optimizing Fourier neural operators (FNOs), a data-driven model capable of simulating complex ocean behaviors. Selecting the correct model and tuning the hyperparameters are challenging tasks, requiring much effort to ensure model accuracy. DeepHyper allows efficient exploration of hyperparameters associated with data preprocessing, FNO architecture-related hyperparameters, and various model training strategies. We aim to obtain an optimal set of hyperparameters leading to the most performant model. Moreover, on top of the commonly used mean squared error for model training, we propose adopting the negative anomaly correlation coefficient as the additional loss term to improve model performance and investigate the potential trade-off between the two terms. The numerical experiments show that the optimal set of hyperparameters enhanced model performance in single timestepping forecasting and greatly exceeded the baseline configuration in the autoregressive rollout for long-horizon forecasting up to 30 days. Utilizing DeepHyper, we demonstrate an approach to enhance the use of FNO in ocean dynamics forecasting, offering a scalable solution with improved precision.

Improving subseasonal forecasting of East Asian monsoon precipitation with deep learning

Accurate subseasonal forecasting of East Asian summer monsoon (EASM) precipitation is crucial, as it directly impacts the livelihoods of billions. However, the prediction skill of state-of-the-art subseasonal-to-seasonal (S2S) models for precipitation remains limited. In this study, the authors developed a convolutional neural network (CNN) regression model to enhance the prediction skill for weekly EASM precipitation by utilizing the more reliably predicted circulation fields from dynamic models. The outcomes of the CNN model are promising, as it led to a 14 % increase in the anomaly correlation coefficient (ACC), from 0.30 to 0.35, and a 22 % reduction in the root-mean-square error (RMSE), from 3.22 to 2.52, for predicting the weekly EASM precipitation index at a leading time of one week. Among the S2S models, the improvement in prediction skill through CNN correction depends on the model's performance in accurately predicting circulation fields. The CNN correction of EASM precipitation index can only rectify the systematic errors of the model and is independent of whether the each grid point or the entire area-averaged index is corrected. Furthermore, u200 (200-hPa zonal wind) is identified as the most important variable for efficient correction.摘要东亚夏季风(EASM)降水的准确次季节预报至关重要, 因为它直接影响着数十亿人的生计. 然而, 最先进的次季节-季节(S2S)预测模型的预测技巧仍然有限. 本研究开发了一种卷积神经网络(CNN)回归模型, 通过利用动力预测模型预测的更可靠的环流场来提高EASM周降水的预测技巧. 经过CNN模型的订正, 在提前一周预测EASM降水指数时, 11个S2S模式的平均距平相关系数从增加了14 %, 从0.30增加到0.35; 均方根误差减少了22 %, 从3.22减少到2.52. 在这些S2S模式中, 通过CNN订正对预测技巧的提高程度取决于模式在准确预测大气环流变量方面的表现. 对EASM降水指数的CNN订正只能订正模式的系统误差, 与逐个网格订正还是整个区域平均指数订正无关, 并且在不同的提前期内CNN的订正效果基本不变. 此外, 200hPa纬向风被认为是有效订正的最重要变量.

Subgrid-Scale Topographic Effects on Radiation for Global Weather Forecast Models

The incoming solar radiation arriving the Earth’s surface is strongly influenced by surface terrain. Conventional global weather forecast models, with grid scales of about 10 km, lack the resolution to accurately capture terrain-induced variations. We devised a new parameterization method to incorporate high-resolution subgrid-scale terrain data into grid-scale radiative flux calculations without averaging or smoothing topographic features. Utilizing a 15″ digital elevation model from the Shuttle Radar Topography Mission, we computed subgrid-scale data and transformed them onto the Korean Integrated Model’s cubed sphere grid using a Voronoi diagram to maintain geographical accuracy. The new scheme was initially evaluated through offline ideal tests and case studies. The results demonstrated that the scheme accurately captured the variations in downward shortwave flux, keeping the mean flux on a global scale nearly constant. The global mean flux difference in all skies was less than 0.01%. Statistical analyses demonstrated improved temperature and geopotential height predictions compared to reanalysis data. The anomaly correlation coefficient for East Asia at 850 hPa increased by 0.036 at 240 forecast hours. Overall, the anomaly correlation coefficient and root mean square error of geopotential height and temperature showed enhancements, particularly in the Northern Hemisphere and tropics. Importantly, the scheme introduces negligible additional memory and CPU requirements, making it suitable for both regional and global models. Only a 0.58% increase in CPU time was observed for the 10-day forecast.

Quantization of Axion-Gauge Couplings and Noninvertible Higher Symmetries.

We derive model-independent quantization conditions on the axion couplings (sometimes known as the anomaly coefficients) to the standard model gauge group [SU(3)×SU(2)×U(1)_{Y}]/Z_{q} with q=1, 2, 3, 6. Using these quantization conditions, we prove that any QCD axion model to the right of the E/N=8/3 line on the |g_{aγγ}|-m_{a} plot must necessarily face the axion domain wall problem in a postinflationary scenario. We further demonstrate the higher-group and noninvertible global symmetries in the standard model coupled to a single axion. These generalized global symmetries lead to universal bounds on the axion string tension and the monopole mass. If the axion were discovered in the future, our quantization conditions could be used to constrain the global form of the standard model gauge group.

Seasonal Prediction of Regional Arctic Sea Ice Using the High‐Resolution Climate Prediction System CMA‐CPSv3

AbstractSea ice is a central part of the Arctic climate system, and its changes have a significant impact on the Earth's climate. Yet, its state, especially in summer, is not fully understood and correctly predicted in dynamical forecast systems. In this study, the seasonal prediction skill of Arctic sea ice is investigated in a high‐resolution dynamical forecast system, the China Meteorological Administration Climate Prediction System (CMA‐CPSv3). A 7‐month‐long retrospective forecast is made every other month from 2001 to 2021. Employing the anomaly correlation coefficient as the metric of the prediction skill, we show that CMA‐CPSv3 can predict regional Arctic sea ice extent and sea ice thickness up to 7 lead months. The Bering Sea exhibits the highest prediction skill among the 14 Arctic subregions. CMA‐CPSv3 outperforms the anomaly persistence forecast in the Bering Sea, Sea of Okhotsk, Laptev Sea, and East Siberian Sea. The sources of the sea ice prediction skill partly come from the good performance of upper ocean temperature in CMA‐CPSv3. This holds true not only for winter sea ice in the Arctic marginal seas but also for summer sea ice in several Arctic central seas. Furthermore, CMA‐CPSv3 exhibits a strong relationship between the variability of sea ice and surface heat fluxes. This underscores the importance of enhancing the representation of air‐sea heat exchanges in dynamical forecast systems to improve the prediction skill of sea ice.

Modular Hamiltonian in the semi infinite line. Part II. Dimensional reduction of Dirac fermions in spherically symmetric regions

In this article, we extend our study on a new class of modular Hamiltonians on an interval attached to the origin on the semi-infinite line, introduced in a recent work dedicated to scalar fields. Here, we shift our attention to fermions and similarly to the scalar case, we investigate the modular Hamiltonians of theories which are obtained through dimensional reduction, this time, of a free massless Dirac field in d dimensions. By following the same methodology, we perform dimensional reduction on both the physical and modular Hamiltonians. This process enables us to establish a correspondence: we identify the modular Hamiltonian in an interval connected to the origin to the one obtained from the reduction of the modular Hamiltonian pertaining to the conformal parent theory on a sphere. Intriguingly, although the resulting one-dimensional theories lack conformal symmetry due to the presence of a term proportional to 1/r, the corresponding modular Hamiltonians are local functions in the energy density. This phenomenon mirrors the well known behaviour observed in the conformal case and indicates the existence of a residual symmetry, characterized by a subset of the original conformal group.Furthermore, through an analysis of the spectrum of the modular Hamiltonians, we derive an analytic expression for the associated entanglement entropy. Our findings also enable us to successfully recover the conformal anomaly coefficient from the universal piece of the entropy in even dimensions, as well as the universal constant F term in d = 3, by extending the radial regularization scheme originally introduced by Srednicki to perform the sum over angular modes.

A Deep Neural Network-Ensemble Adjustment Kalman Filter and Its Application on Strongly Coupled Data Assimilation

This paper introduces a novel ensemble adjustment Kalman filter (EAKF) that integrates a machine-learning approach. The conventional EAKF adopts linear and Gaussian assumptions, making it difficult to handle cross-component updates in strongly coupled data assimilation (SCDA). The new approach employs nonlinear variable relationships established by a deep neural network (DNN) during the analysis stage of the EAKF, which nonlinearly projects observation increments into the state variable space. It can diminish errors in estimating cross-component error covariance arising from insufficient ensemble members, therefore improving the SCDA analysis. A conceptual coupled model is employed in this paper to conduct twin experiments, validating the DNN–EAKF’s capability to outperform conventional EAKF in SCDA. The results reveal that the DNN–EAKF can make SCDA superior to WCDA with a limited ensemble size. The root-mean-squared errors are reduced up to 70% while the anomaly correlation coefficients are increased up to 20% when the atmospheric observations are used to update the ocean variables directly. The other model components can also be improved through SCDA. This approach is anticipated to offer insights for future methodological integrations of machine learning and data assimilation and provide methods for SCDA applications in coupled general circulation models.

Features of forecasting abnormally high reservoir pressures when drilling wells in the areas of Southwestern Turkmenistan

The article provides information on changes in reservoir pressure gradients in the stratigraphic section of strata with increasing depth in the oil and gas fields of the Baltic and Gogerendag-Ekerem zones. The conditions for the formation of abnormally high reservoir pressures occurring are given, as well as the classification of reservoir pressures by the anomaly coefficient. Reservoir pressure of horizons is predicted based on the results of drilling deep wells. This work is useful and can be used to fulfill the tasks set when drilling new deep wells with abnormally high reservoir pressures for their uncomplicated successful completion of construction. Keywords: miocene; drilling speed; drilling mud; hydrodynamics; well.

On torsion contribution to chiral anomaly via Nieh–Yan term

In this note we present a solution to the question of whether or not, in the presence of torsion, the topological Nieh–Yan term contributes to chiral anomaly. The integral of Nieh–Yan term is non-zero if topology is non-trivial; the manifold has a boundary or vierbeins have singularities. Noting that singular Nieh–Yan term could be written as a sum of delta functions, we argue that the heat kernel expansion cannot end at finite steps. This leads to a sinusoidal dependence on the Nieh–Yan term and the UV cut-off of the theory (or alternatively the minimum length of spacetime). We show this ill-behaved dependence can be removed if a quantization condition on length scales is applied. It is expected as the Nieh–Yan term can be derived as the difference of two Chern class integrals (i.e. Pontryagin terms). On the other hand, in the presence of a cosmological constant, we find that indeed the Nieh–Yan term contributes to the index with a dimensionful anomaly coefficient that depends on the de Sitter length or equivalently inverse Hubble rate. We find similar result in thermal field theory where the anomaly coefficient depends on temperature. In both examples, the anomaly coefficient depends on IR cut-off of the theory. Without singularities, the Nieh–Yan term can be smoothly rotated away, does not contribute to topological structure and consequently does not contribute to chiral anomaly.

RG flows on two-dimensional spherical defects

We study two-dimensional spherical defects in d-dimensional Conformal Field Theories. We argue that the Renormalization Group (RG) flows on such defects admit the existence of a decreasing entropy function. At the fixed points of the flow, the entropy function equals the anomaly coefficient which multiplies the Euler density in the defect’s Weyl anomaly. Our construction demonstrates an alternative derivation of the irreversibility of RG flows on two-dimensional defects. Moreover, in the case of perturbative RG flows induced by weakly relevant deformations, the entropy function decreases monotonically and plays the role of a C-function. We provide a simple example to explicitly work out the RG flow details in the proposed construction.

Evaluating the Detection of Oceanic Mesoscale Eddies in an Operational Eddy-Resolving Global Forecasting System

In this study, a global analysis and forecasting system at 1/12° is built for operational oceanography at the National Marine Environmental Forecasting Center (NMEFC) by using the NEMO ocean model (NMEFC-NEMO). First, statistical analysis methods are designed to evaluate the performance of sea level anomaly (SLA) forecasting. The results indicate that the NMEFC-NEMO performs well in SLA forecasting when compared with the Mercator-PSY4, Mercator-PSY3, UK-FOAM, CONCEPTS-GIOPS and Bluelink-OceanMAPS forecasting systems. The respective root-mean-squared errors (RMSEs) of NMEFC-NEMO (Mercator PSY4) are 0.0654 m (0.0663 m) and 0.0797 m (0.0767 m) for the lead times of 1 and 7 days. The anomaly correlation coefficients between forecasting and observations exceed 0.8 for the NMEFC-NEMO and Mercator-PSY4 systems, suggesting that the accuracy of SLA predicted using NMEFC-NEMO is comparable to Mercator PSY4 and superior to other forecasting systems. Moreover, the global spatial distribution of oceanic eddies are effectively represented in NMEFC-NEMO when compared to that in the HYCOM reanalysis, and the detection rate reaches more than 90% relative to HYCOM reanalysis. Regarding the strong eddies in the Kuroshio region, the NMEFC-NEMO reproduces the characteristic for anticyclonic and cyclonic eddies merging and splitting alternatively. As for the detective eddies in the Gulf Stream, NMEFC-NEMO effectively represents the spatial distribution of mesoscale eddies from different seasons. The amplitude of oceanic eddies, including both cyclones and anticyclones, were much stronger on 1 July 2019 than 1 January 2019. Overall, NMEFC-NEMO has a superior performance in SLA forecasting and effectively represents the oceanic mesoscale eddies for operational oceanography.

Difference in boreal winter predictability between two dynamical cores of Community Atmosphere Model 5

This study investigates the sensitivity of the boreal winter prediction skill of Community Atmosphere Model 5 to the choice of the dynamical core. Both finite volume (FV) and spectral element (SE) dynamical cores are tested. An additional FV with the SE topography (FVSE) is also conducted to isolate the possible influence of the topography. The three dynamical core experiments, which ran from 2001/2002–2017/2018, are validated using Japanese 55 year reanalysis data. It turns out that the SE (−4.27 °C) has a smaller cold bias in boreal-winter surface air temperature (SAT) than the FV (−5.17 °C) and FVSE (−5.29 °C), particularly in North America, East Asia, and Southern Europe/Northern Africa. Significant North Atlantic Oscillation-like biases are also identified in the mid-troposphere. These biases affect seasonal prediction skills. Although the overall prediction skills of boreal-winter SAT, quantified by the anomaly correlation coefficient (ACC), and root-mean-square error (RMSE), are reasonably good (ACC = 0.40 and RMSE = 0.47 in the mean values of SE, FV, and FVSE), they significantly differ from one region to another, depending on the choice of dynamical cores. For North America and Southern Europe/Northern Africa, SE shows better skills than FVSE and FV. Conversely, in East Asia, FV and FVSE outperform SE. These results suggest that the appropriate choice of the dynamical cores and the bottom boundary conditions could improve the boreal-winter seasonal prediction on a regional scale.

Features of forecasting abnormally high reservoir pressures when drilling wells in areas of south-western Turkmenistan

Introduction: The study is essential because predicting areas with high formation pressures in geophysical research is complex. It requires new technologies to accurately locate them and minimize unforeseen emergencies. The purpose of this study is to analyse and identify the peculiarities of predicting anomalously high formation pressures during well drilling in the territory of south-western Turkmenistan in order to provide more effective and reliable pressure management during drilling and operation. Materials and Methods: Methods such as analysis and prediction were used in this study. Results and Discussion: The paper presents valuable research data highlighting the changes in reservoir pressure gradients in the unique stratigraphic section of reservoirs in the oil and gas fields of the Pribalkan and Gogerendag-Ekerem zones. The analysis of the studies reveals the dynamism with which the properties of reservoir rock change with increasing depth of occurrence. Special attention is paid to the processes associated with the formation of anomalously high formation pressures, which is an important aspect for understanding complex geological processes. Conclusions: The study provides a detailed classification of formation pressures, considering the anomaly coefficient, enhancing our understanding of this phenomenon. Predicting formation pressures for specific horizons relies on analyzing deep well drilling results.