Investigation of GRACE-derived Information on Forest Drought Stress Across the Contiguous US

This research derives z-score monthly groundwater storage (GWS) anomalies and z-score monthly root zone soil moisture (RZSM) anomalies from products of Gravity Recovery and Climate Experiment Data Assimilation (GRACE-DA). Z-score monthly GWS and RZSM anomalies are compared to two drought indicators: Standardized Precipitation Index (SPI) and Standardized Precipitation-Evapotranspiration Index (SPEI) to investigate the usefulness of GRACE-DA information to detect drought conditions at tree-ring sites. This study also compares z-score monthly GWS and RZSM anomalies with the Tree Ring Standardized Growth Index (TRSGI) that is resampled by bootstrapping to investigate the capability of monitoring forest drought stress. Finally, this research uses multiple linear regression to develop a model for predicting tree-ring widths at selected study sites. The results of the comparisons of z-score monthly GWS and RZSM anomalies and commonly-used drought indices (SPI and SPEI) indicate that GWS anomalies have strong correlations (\u3e 0.4) with long-term droughts (\u3e 9 months) and RZSM anomalies have strong correlations (\u3e 0.5) with short-term droughts (\u3c 3 months). The results of comparisons of TRSGI suggest that z-score monthly GWS and RZSM anomalies are significantly related to tree-ring widths with a significant level of 0.05. This research suggests that the relationships between GWS anomalies and drought indices (SPI and SPEI) and TRSGI highly depend on the geological formations, such as the types of the aquifers, and geographical environments such as the soil texture. The multiple linear regression in this paper quantifies the impacts of z-score monthly GWS and RZSM anomalies on tree-ring widths, which suggests GRACE-DA products can provide useful information to detect and predict the growth of trees. The results also suggest the predictor, monthly RZSM anomalies, is one of the most important parameters in the regression model. Overall, the study suggests that GRACE-DA information can be used to help detect and monitor the stress from drought impacts on trees at a large spatial scale. Adviser: Tsegaye Tadess

Visible and Near Infrared Image Fusion Based on Texture Information

Multi-sensor fusion is widely used in the environment perception system of the autonomous vehicle. It solves the interference caused by environmental changes and makes the whole driving system safer and more reliable. In this paper, a novel visible and near-infrared fusion method based on texture information is proposed to enhance unstructured environmental images. It aims at the problems of artifact, information loss and noise in traditional visible and near infrared image fusion methods. Firstly, the structure information of the visible image (RGB) and the near infrared image (NIR) after texture removal is obtained by relative total variation (RTV) calculation as the base layer of the fused image; secondly, a Bayesian classification model is established to calculate the noise weight and the noise information and the noise information in the visible image is adaptively filtered by joint bilateral filter; finally, the fused image is acquired by color space conversion. The experimental results demonstrate that the proposed algorithm can preserve the spectral characteristics and the unique information of visible and near-infrared images without artifacts and color distortion, and has good robustness as well as preserving the unique texture.Comment: 10 pages,11 figure

Quantitative Assessment of Drought Impacts Using XGBoost based on the Drought Impact Reporter

Under climate change, the increasing frequency, intensity, and spatial extent of drought events lead to higher socio-economic costs. However, the relationships between the hydro-meteorological indicators and drought impacts are not identified well yet because of the complexity and data scarcity. In this paper, we proposed a framework based on the extreme gradient model (XGBoost) for Texas to predict multi-category drought impacts and connected a typical drought indicator, Standardized Precipitation Index (SPI), to the text-based impacts from the Drought Impact Reporter (DIR). The preliminary results of this study showed an outstanding performance of the well-trained models to assess drought impacts on agriculture, fire, society & public health, plants & wildlife, as well as relief, response & restrictions in Texas. It also provided a possibility to appraise drought impacts using hydro-meteorological indicators with the proposed framework in the United States, which could help drought risk management by giving additional information and improving the updating frequency of drought impacts. Our interpretation results using the Shapley additive explanation (SHAP) interpretability technique revealed that the rules guiding the predictions of XGBoost comply with domain expertise knowledge around the role that SPI indicators play around drought impacts.Comment: 4 pages with 2 figures and 1 table. NeurIPS workshop on Tackling Climate Change with Machine Learning, 202

Bridging the Gap:Cross-modal Knowledge Driven Network for Radiology Report Generation

Radiology report generation aims to generate medical reports based on given medical images, which can alleviate the workload of radiologists and has attracted significant research interest in recent years. However, existing studies have struggled to bridge the gap between the two different modalities (i.e. image and text) and generate clinically accurate reports. This is primarily due to the challenges in modelling the crossmodal mappings and the inefficiency of transferring knowledge across modalities. To address these challenges, in this paper, we propose to leverage a pre-constructed knowledge graph as a shared matrix that bridges the gap between visual and textual information, facilitating cross-modal knowledge transfer. This shared knowledge matrix effectively captures cross-modal mappings and aligns information between images and texts, thereby bridging the gap between modalities. Specifically, we propose a new module for knowledge distillation and preservation that integrates relevant knowledge representations into both visual and textual inputs, facilitating intuitive cross-modal knowledge interaction and enhancing the clinical accuracy of the generated reports. Experimental results on two benchmark datasets show the effectiveness of our method, outperforming state-of-the-arts in report generation

TweetDrought: A Deep-Learning Drought Impacts Recognizer based on Twitter Data

Acquiring a better understanding of drought impacts becomes increasingly vital under a warming climate. Traditional drought indices describe mainly biophysical variables and not impacts on social, economic, and environmental systems. We utilized natural language processing and bidirectional encoder representation from Transformers (BERT) based transfer learning to fine-tune the model on the data from the news-based Drought Impact Report (DIR) and then apply it to recognize seven types of drought impacts based on the filtered Twitter data from the United States. Our model achieved a satisfying macro-F1 score of 0.89 on the DIR test set. The model was then applied to California tweets and validated with keyword-based labels. The macro-F1 score was 0.58. However, due to the limitation of keywords, we also spot-checked tweets with controversial labels. 83.5% of BERT labels were correct compared to the keyword labels. Overall, the fine-tuned BERT-based recognizer provided proper predictions and valuable information on drought impacts. The interpretation and analysis of the model were consistent with experiential domain expertise

Evaluating and Improving Tool-Augmented Computation-Intensive Math Reasoning

Chain-of-thought prompting~(CoT) and tool augmentation have been validated in recent work as effective practices for improving large language models~(LLMs) to perform step-by-step reasoning on complex math-related tasks. However, most existing math reasoning datasets may be not able to fully evaluate and analyze the ability of LLMs in manipulating tools and performing reasoning, as they may only require very few invocations of tools or miss annotations for evaluating intermediate reasoning steps. To address the issue, we construct \textbf{CARP}, a new Chinese dataset consisting of 4,886 computation-intensive algebra problems with formulated annotations on intermediate steps. In CARP, we test four LLMs with CoT prompting, and find that they are all prone to make mistakes at the early steps of the solution, leading to wrong answers. Based on this finding, we propose a new approach that can deliberate the reasoning steps with tool interfaces, namely \textbf{DELI}. In DELI, we first initialize a step-by-step solution based on retrieved exemplars, then iterate two deliberation procedures that check and refine the intermediate steps of the generated solution, from the perspectives of tool manipulation and natural language reasoning, until obtaining converged solutions or reaching the maximum turn. Experimental results on CARP and six other datasets show that the proposed DELI mostly outperforms competitive baselines, and can further boost the performance of existing CoT methods. Our data and code are available in \url{https://github.com/RUCAIBox/CARP}.Comment: 17 pages, working in progres

A conjugate study of the polar ionospheric F2-layer and IRI-2007 at 75° magnetic latitude for solar minimum

Long-duration conjugate observations by the EISCAT Svalbard Radar (ESR) and the ionosonde at Zhongshan station from the International Polar Year (IPY) during solar minimum conditions are analyzed, with respect to variability in the F2-layer peak parameters. A comparison between International Reference Ionosphere- 2007 (IRI-2007) and observation data clearly demonstrates good agreement in summer, but greater deviations in winter. The IRI model reproduces the F2 peak parameters dominated by solar photoionization reasonably well, but it does not address the effect of electron precipitation. Hence, the discrepancies become large in the winter auroral ionosphere

Statistical characteristics of ionospheric backscatter observed by SuperDARN Zhongshan radar in Antarctica

Zhongshan HF radar, as one component of SuperDARN, has been established and in operation since April, 2010. Using data from the first two years of its operation, this paper investigates the radar’s performance, the diurnal and seasonal variations of ionospheric echoes, and their dependence on geomagnetic activity. Statistical studies show that the occurrence of echoes in different beams varies at different frequencies, which arises from the direction of the beam and the area over which the beam can achieve the orthogonality condition between the wave vector and the Earth’s magnetic field. The diurnal variation is obvious with double peak structures both in the occurrence rate and average power at 04–08 UT and 16–17 UT. The line-of-sight velocities are mainly positive on the dayside and negative on the nightside for Beam 0, which is the opposite of the trend for Beam 15. The spectral widths on the dayside are often higher than those on the nightside owing to the high energy particle precipitation in the cusp region. The seasonal variations are more obvious for those beams with larger numbers. The occurrence, the average power, the line-of-sight velocity, and the spectral widths are generally larger in the winter months than in the summer months. The influence of geomagnetic activity on radar echoes is significant. The peak echo occurrence appears on the dayside during geomagnetically quiet times, and shifts toward the nightside and exhibits an obvious decrease with increasing Kp. With increasing geomagnetic activity, the line-of-sight velocities increase, whereas the spectral widths decrease. The frequency dependence is investigated and it is found that in the operating frequency bands in 2010, 9–10 MHz is the most appropriate band for the SuperDARN Zhongshan radar