Information Analysis of Catchment Hydrologic Patterns across Temporal Scales

Catchment hydrologic cycle takes on different patterns across temporal scales. The interim between event-scale hydrologic process and mean annual water-energy correlation pattern requires further examination to justify self-consistent understanding. In this paper, the temporal scale transition revealed by observation and simulation was evaluated in an information theoretical framework named Aleatory Epistemic Uncertainty Estimation. The Aleatory Uncertainty refers to posterior uncertainty of runoff given the input variables’ observations. The Epistemic Uncertainty refers to the posterior uncertainty increase due to the imperfect observation decoding in models. Daily hydrometeorological observations in 24 catchments were aggregated from 10 days to 1 year before implementing the information analysis. Estimations of information contents and flows of hydrologic terms across temporal scales were related with the catchments’ seasonality type. It also showed that information distilled by the monthly and annual water balance models applied here did not correspond to that provided by observations around temporal scale from two months to half a year. This calls for a better understanding of seasonal hydrologic mechanism

Reconstructing Three-decade Global Fine-Grained Nighttime Light Observations by a New Super-Resolution Framework

Satellite-collected nighttime light provides a unique perspective on human activities, including urbanization, population growth, and epidemics. Yet, long-term and fine-grained nighttime light observations are lacking, leaving the analysis and applications of decades of light changes in urban facilities undeveloped. To fill this gap, we developed an innovative framework and used it to design a new super-resolution model that reconstructs low-resolution nighttime light data into high resolution. The validation of one billion data points shows that the correlation coefficient of our model at the global scale reaches 0.873, which is significantly higher than that of other existing models (maximum = 0.713). Our model also outperforms existing models at the national and urban scales. Furthermore, through an inspection of airports and roads, only our model's image details can reveal the historical development of these facilities. We provide the long-term and fine-grained nighttime light observations to promote research on human activities. The dataset is available at \url{https://doi.org/10.5281/zenodo.7859205}

Fault diagnosis of rolling bearing using CVA based detector

There are two key problems in bearing fault diagnosis that need to be addressed, one is feature selection, the other is faulty dataset problem. On the one hand, signal decomposition methods are popular ways to decompose signal into a number of modes of interest, while the most interesting modes need to be selected to represent original signal. This procedure may easily lead to loss of important information. On the other hand, most of works adopt the faulty data to train fault diagnosis classifier, while the faulty data sets are difficult to collect in real life. Hence many existing methods are unsuitable for practical application. Moreover, a high number of researchers introduce various hybrid methods to improve the ability of original methods, which increases the complexity of fault diagnosis. To solve these problems, firstly, a canonical variate analysis (CVA) detector based on visual inspection is proposed to classify operating states. Healthy dataset obtained under normal condition is applied for building a reference model and generating a threshold. CVA transforms the unknown variable into state space and residual space, then T2 and Q metrics are used to capture the variation in the two spaces, respectively. The metrics of variable compared with reference model will determine the state of rolling bearing. Considering that the threshold of proposed detector is likely to be exceeded, and visual inspection fails to identify bearing fault automatically. Then the means of T2 and Q metrics are presented to enlarge the distance between normal and abnormal conditions to avoid those drawbacks. At last, experiment and comparison are conducted to verify the capability of the proposed work. The results demonstrate that the proposed work is simple and effective in bearing fault diagnosis

Estimating Warehouse Rental Price using Machine Learning Techniques

Boosted by the growing logistics industry and digital transformation, the sharing warehouse market is undergoing a rapid development. Both supply and demand sides in the warehouse rental business are faced with market perturbations brought by unprecedented peer competitions and information transparency. A key question faced by the participants is how to price warehouses in the open market. To understand the pricing mechanism, we built a real world warehouse dataset using data collected from the classified advertisements websites. Based on the dataset, we applied machine learning techniques to relate warehouse price with its relevant features, such as warehouse size, location and nearby real estate price. Four candidate models are used here: Linear Regression, Regression Tree, Random Forest Regression and Gradient Boosting Regression Trees. The case study in the Beijing area shows that warehouse rent is closely related to its location and land price. Models considering multiple factors have better skill in estimating warehouse rent, compared to singlefactor estimation. Additionally, tree models have better performance than the linear model, with the best model (Random Forest) achieving correlation coefficient of 0.57 in the test set. Deeper investigation of feature importance illustrates that distance from the city center plays the most important role in determining warehouse price in Beijing, followed by nearby real estate price and warehouse size

Diffusion Model-based Probabilistic Downscaling for 180-year East Asian Climate Reconstruction

As our planet is entering into the "global boiling" era, understanding regional climate change becomes imperative. Effective downscaling methods that provide localized insights are crucial for this target. Traditional approaches, including computationally-demanding regional dynamical models or statistical downscaling frameworks, are often susceptible to the influence of downscaling uncertainty. Here, we address these limitations by introducing a diffusion probabilistic downscaling model (DPDM) into the meteorological field. This model can efficiently transform data from 1{\deg} to 0.1{\deg} resolution. Compared with deterministic downscaling schemes, it not only has more accurate local details, but also can generate a large number of ensemble members based on probability distribution sampling to evaluate the uncertainty of downscaling. Additionally, we apply the model to generate a 180-year dataset of monthly surface variables in East Asia, offering a more detailed perspective for understanding local scale climate change over the past centuries

Advancing Precipitation Prediction Using a Composite of Models and Data

Advances in numerical weather forecasts have brought forward considerable societal benefits and raised expectations for higher resolution, more accurate, and longer predictions. Despite the consistent progresses achieved, the prediction of precipitation remains a less satisfyingly tackled task, with skills falling far behind those of other atmospheric variables. This dissertation serves as an inspection of prediction capacity and an exploration of predictability for the precipitation process, with a particular focus on the region of West Coast United States.The sources of predictability, accuracy requirements, and optimal model configurations are distinct regarding the considered forecasting scales and ranges. To identify the successes and deficiencies in predictions and benchmark further advances, a seamless assessment of precipitation prediction skill for short range up to subseasonal scale range is conducted. The evaluation is based on the Subseasonal-to-Seasonal Prediction Project retrospective forecast database. The prediction skill–lead time relationship is evaluated, using multiple models, and measured by both deterministic and probabilistic skill scores. Results show advantageous deterministic skills for the evaluated models at Week-1. The best-performing models achieved r ≈ 0.6 for Week-2 predictions.The potential sources of predictability at extended range from some of the key climate variations are investigated based on a composite of statistical evidences and numerical predictions. Results show that periods of heavy precipitation associated with ENSO are more pre- dictable at the extended range period. The excessive precipitation and improved extended- range prediction skill during ENSO periods are attributed to the meridional shift of baroclinic systems as modulated by ENSO. Through examining precipitation anomalies conditioned on the MJO, I verified that active MJO events systematically modulate the area’s precipita- tion distribution. Most of the evaluated models are still struggling to represent the MJO or its associated teleconnections, especially at phases 3–4. However, some models do exhibit enhanced extended-range prediction skills under active MJO conditions.The advantageous precipitation prediction skill for short to medium range originates from a steady accumulation of scientific achievements in (1) inferring atmospheric initial states, (2) resolving atmospheric fluid dynamics, and (3) approximating unresolved atmospheric processes. Evaluation results suggest that we have not fully realized the potentials of these advances in fostering a corresponding improvement in precipitation prediction. Here, the old art of forecasting by reading weather chart and advances in deep learning for image recognition are combined to shed light on the precipitation prediction task from a top-down, data-driven viewpoint. A deep convolutional neural network (CNN) model is trained to learn precipitation-related dynamical features from the surrounding dynamical and moisture fields by optimizing a hierarchical set of spatial convolution kernels. The model applies an “end-to- end” learning strategy to automatically search, synthesize, and extract salient spatial features from the resolved high-dimensional atmospheric field for accurate precipitation estimation at daily scale. Experiments for different regions across the contiguous United States show that, provided with enough data, precipitation estimates from the CNN model outperform the reanalysis precipitation products, as well as the statistical downscaling products using linear regression, nearest neighbor, random forest, or fully-connected deep neural network.The idea of “end-to-end” learning for inferring unresolved precipitation process based on resolved atmospheric field is further explored for hourly scale quantitative precipitation fore- cast. Hourly precipitation observations from various sources are collected, quality controlled, and concatenated to compose a unique long-term (1980/1/1- 2018/12/31) high temporal resolution precipitation observation dataset. A general framework for statistically modeling of spatiotemporal data and making use of inconsistently available observations is developed.Hourly precipitation predictions using the deep neural network model give r ≈ 0.8 at 2◦ ×2.5◦ spatial scale, while the baseline numerical model achieved r ≈ 0.5. The best performance at hourly, gauge-point scale reaches the order of r ≈ 0.6 for some gauges. However, there is high skill variance in estimating precipitation at such a stringent spatiotemporal resolution. To further test the proposed model in practical forecasts, dynamical retrospective forecast experiments for two atmospheric river land-falling events are carried out using the Weather Research and Forecasting (WRF) model. The WRF dynamical simulations are used to force the trained neural network model for alternative precipitation process predictions. Simulation results verified the consistency and robustness of the proposed approach. It should be noted that the methods here are not intended to replace precipitation-related parameterization schemes using a “black box” model, rather, the target is to set a benchmark for precipitation prediction from a data-driven perspective, and offer directions for improving precipitation related parameterizations.Overall, this work conducted a systematical evaluation of precipitation prediction skills across a spectrum of critical scales and ranges. Sources of predictability at subseasonal scale are explored based on a composite of statistical analysis and numerical prediction. The potential of deep learning for seeking evidences in improving precipitation prediction is explored by combining high quality observation data with numerical dynamical predictions

Advancing Precipitation Prediction Using a Composite of Models and Data

Advances in numerical weather forecasts have brought forward considerable societal benefits and raised expectations for higher resolution, more accurate, and longer predictions. Despite the consistent progresses achieved, the prediction of precipitation remains a less satisfyingly tackled task, with skills falling far behind those of other atmospheric variables. This dissertation serves as an inspection of prediction capacity and an exploration of predictability for the precipitation process, with a particular focus on the region of West Coast United States.The sources of predictability, accuracy requirements, and optimal model configurations are distinct regarding the considered forecasting scales and ranges. To identify the successes and deficiencies in predictions and benchmark further advances, a seamless assessment of precipitation prediction skill for short range up to subseasonal scale range is conducted. The evaluation is based on the Subseasonal-to-Seasonal Prediction Project retrospective forecast database. The prediction skill–lead time relationship is evaluated, using multiple models, and measured by both deterministic and probabilistic skill scores. Results show advantageous deterministic skills for the evaluated models at Week-1. The best-performing models achieved r ≈ 0.6 for Week-2 predictions.The potential sources of predictability at extended range from some of the key climate variations are investigated based on a composite of statistical evidences and numerical predictions. Results show that periods of heavy precipitation associated with ENSO are more pre- dictable at the extended range period. The excessive precipitation and improved extended- range prediction skill during ENSO periods are attributed to the meridional shift of baroclinic systems as modulated by ENSO. Through examining precipitation anomalies conditioned on the MJO, I verified that active MJO events systematically modulate the area’s precipita- tion distribution. Most of the evaluated models are still struggling to represent the MJO or its associated teleconnections, especially at phases 3–4. However, some models do exhibit enhanced extended-range prediction skills under active MJO conditions.The advantageous precipitation prediction skill for short to medium range originates from a steady accumulation of scientific achievements in (1) inferring atmospheric initial states, (2) resolving atmospheric fluid dynamics, and (3) approximating unresolved atmospheric processes. Evaluation results suggest that we have not fully realized the potentials of these advances in fostering a corresponding improvement in precipitation prediction. Here, the old art of forecasting by reading weather chart and advances in deep learning for image recognition are combined to shed light on the precipitation prediction task from a top-down, data-driven viewpoint. A deep convolutional neural network (CNN) model is trained to learn precipitation-related dynamical features from the surrounding dynamical and moisture fields by optimizing a hierarchical set of spatial convolution kernels. The model applies an “end-to- end” learning strategy to automatically search, synthesize, and extract salient spatial features from the resolved high-dimensional atmospheric field for accurate precipitation estimation at daily scale. Experiments for different regions across the contiguous United States show that, provided with enough data, precipitation estimates from the CNN model outperform the reanalysis precipitation products, as well as the statistical downscaling products using linear regression, nearest neighbor, random forest, or fully-connected deep neural network.The idea of “end-to-end” learning for inferring unresolved precipitation process based on resolved atmospheric field is further explored for hourly scale quantitative precipitation fore- cast. Hourly precipitation observations from various sources are collected, quality controlled, and concatenated to compose a unique long-term (1980/1/1- 2018/12/31) high temporal resolution precipitation observation dataset. A general framework for statistically modeling of spatiotemporal data and making use of inconsistently available observations is developed.Hourly precipitation predictions using the deep neural network model give r ≈ 0.8 at 2◦ ×2.5◦ spatial scale, while the baseline numerical model achieved r ≈ 0.5. The best performance at hourly, gauge-point scale reaches the order of r ≈ 0.6 for some gauges. However, there is high skill variance in estimating precipitation at such a stringent spatiotemporal resolution. To further test the proposed model in practical forecasts, dynamical retrospective forecast experiments for two atmospheric river land-falling events are carried out using the Weather Research and Forecasting (WRF) model. The WRF dynamical simulations are used to force the trained neural network model for alternative precipitation process predictions. Simulation results verified the consistency and robustness of the proposed approach. It should be noted that the methods here are not intended to replace precipitation-related parameterization schemes using a “black box” model, rather, the target is to set a benchmark for precipitation prediction from a data-driven perspective, and offer directions for improving precipitation related parameterizations.Overall, this work conducted a systematical evaluation of precipitation prediction skills across a spectrum of critical scales and ranges. Sources of predictability at subseasonal scale are explored based on a composite of statistical analysis and numerical prediction. The potential of deep learning for seeking evidences in improving precipitation prediction is explored by combining high quality observation data with numerical dynamical predictions

A Novel Calibrator for Electronic Transformers Based on IEC 61850

It is necessary for electronic transformer to make calibration before putting it into practice. To solve the problems in actual calibration process, a novel electronic transformer calibrator is designed. In principle, this system adopts both the direct method and the difference method, which are two popular methods for electronic transformer calibration, by this way the application of the system is extended with its reliability improved. In the system design, based on virtual instrument technology, LabVIEW and WinPCap toolkit are used to develop the application software, and it is able to calibrate those electronic transformers following the standard of IEC 61850. In the calculation of ratio and phase error based on fast Fourier transform, a new window function is introduced, and thus the accuracy of calibration, influenced by the frequency vibration, is improved. This research provides theoretic support and practical reference to the development of intelligent calibrator for electronic transformers. DOI: http://dx.doi.org/10.11591/telkomnika.v11i3.232