Meteorological Applications最新文献

{"title":"Local land-use decisions drive losses in river biological integrity to 2099: Using machine learning to disentangle interacting drivers of ecological change in policy forecasts","authors":"Kimberly Bourne,&nbsp;Ryan S. D. Calder,&nbsp;Shan Zuidema,&nbsp;Celia Y. Chen,&nbsp;Mark E. Borsuk","doi":"10.1002/met.70024","DOIUrl":"https://doi.org/10.1002/met.70024","url":null,"abstract":"<p>Climate and land-use/land-cover (LULC) change each threaten the health of rivers. Rising temperatures, changes in rainfall and runoff, and other perturbations, will all impact rivers' physical, biological, and chemical characteristics over the next century. While scientists and policymakers have increasing access to climate and LULC forecasts, the implications of each for outcomes of interest have been difficult to quantify. This is partially because climate and LULC perturb ecological outcomes via incompletely understood site-specific, interacting, and nonlinear mechanisms that are not well suited to analysis using classical statistical methods. This creates uncertainties over the benefits of local-level interventions such as green infrastructure investments and urban densification, and limits how forecasts can be used to inform decision-making. Here, we demonstrate how machine learning can be used to quantify the relative contributions of LULC and climate drivers to impacts on riverine health as measured by taxonomic richness of the macroinvertebrate orders <i>Ephemeroptera</i>, <i>Plecoptera</i>, and <i>Trichoptera</i> (EPT). We develop a cross-validated Random Forest (RF) model to link EPT taxa richness to meteorological, water quality, hydrologic, and LULC variables in watersheds in New Hampshire and Vermont, USA. Prospective climate and LULC scenarios are used to generate predictions of these variables and of EPT taxa richness trends through the year 2099. The model structure is mechanistically interpretable and performs well on test data (<i>R</i>² ~ 0.4). Impacts on EPT taxa richness are driven by local LULC policy such as increased suburbanization. Future trends are likely to be exacerbated by climate change, although warming conditions suggest possible increases in springtime EPT taxa richness. Overall, this analysis highlights (1) the impact of local LULC decisions on riverine health in the context of a changing climate, and (2) the role machine learning methods can play in developing models that disentangle interacting physical mechanisms to advance decision support.</p>","PeriodicalId":49825,"journal":{"name":"Meteorological Applications","volume":"32 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/met.70024","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143112762","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Applications of reanalyses in climate services","authors":"Carlo Buontempo,&nbsp;Chiara Cagnazzo,&nbsp;Dick Dee","doi":"10.1002/met.70026","DOIUrl":"https://doi.org/10.1002/met.70026","url":null,"abstract":"&lt;p&gt;Climate re-analyses are generated by combining Earth system models with meteorological observations, using methods that are similar to those used for numerical weather prediction (NWP). Reanalysis datasets contain a wealth of information about past weather and the recent climate, in the form of multi-decadal time series for many geophysical variables on global grids. Several major NWP centres intermittently conduct reanalysis projects as part of their research and development activities. Reanalysis data have been widely used by the scientific community, especially in the Earth sciences, as evidenced by the very high number of citations of reanalysis products in the published literature.&lt;/p&gt;&lt;p&gt;The quality and utility of reanalysis products have improved greatly over the years, mainly due to steady progress in modelling, Earth observation and data assimilation. At the same time, as society awakens to the real consequences of climate change, demand for reliable information about weather and climate has increased rapidly. Reanalysis data, together with other types of climate data, are now routinely used to assess past, present and future impacts of climate change in agriculture, water resources, energy, health, urban planning, transport and other sectors. Consequently, the community of reanalysis users is growing and becoming much more diverse, with experts in different domains, technical consultants, data scientists and many others who are climate-literate but may not be specialized in climate science.&lt;/p&gt;&lt;p&gt;Having the needs and requirements of this new user community in mind, the Copernicus Climate Change Service (C3S; Buontempo et al., &lt;span&gt;2022&lt;/span&gt;) was designed to facilitate and support development of effective climate services based on high-quality, consistent, scientific data. The service focuses on simplifying access to data and enabling new applications for planners, policy makers and technical experts in the private and public spheres. The backbone of C3S is the Climate Data Store (CDS), which provides open and free access to a catalogue of more than 150 quality-controlled climate datasets, including observations, reanalysis products, climate predictions and climate projections. The CDS has currently more than 325,000 registered users from around the world, who collectively download and process more than 1 Petabyte of data every day.&lt;/p&gt;&lt;p&gt;The most-used CDS dataset by far is the ERA5 reanalysis (Hersbach et al., &lt;span&gt;2020&lt;/span&gt;), produced and maintained by the European Centre for Medium-Range Weather Forecasts (ECMWF). ERA5 provides hourly estimates for a wide range of atmospheric, land and oceanic climate variables on a 31-km global grid, for the period from 1940 to present. The reanalysis continues to be extended forward in time, with daily updates made available to users within 5 days of real time. ERA5 is used by C3S to monitor essential climate variables such as air temperature, precipitation and sea ice and serves as a primar","PeriodicalId":49825,"journal":{"name":"Meteorological Applications","volume":"32 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/met.70026","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143113020","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Estimating latent heat flux of subtropical forests using machine learning algorithms","authors":"Harekrushna Sahu,&nbsp;Pramit Kumar Deb Burman,&nbsp;Palingamoorthy Gnanamoorthy,&nbsp;Qinghai Song,&nbsp;Yiping Zhang,&nbsp;Huimin Wang,&nbsp;Yaoliang Chen,&nbsp;Shusen Wang","doi":"10.1002/met.70023","DOIUrl":"https://doi.org/10.1002/met.70023","url":null,"abstract":"<p>Latent heat flux (LE) is a measure of the water exchange between Earth's surface and atmosphere, also known as evapotranspiration. It is a fundamental component in the Earth's energy budget and hydrological cycle and plays an important role in regulating the weather and climate. Moderate Resolution Imaging Spectroradiometer (MODIS) offers a gap-filled biophysical product for LE at 8-day temporal and 500-meter spatial resolutions. Nonetheless, validation against the in situ eddy covariance measurement reveals significant errors in MODIS LE estimation. Our study integrates ground-measured, reanalysis and satellite data to predict LE by leveraging the advantage of the data-driven method. The study draws upon flux data derived from the AsiaFlux database, alongside reanalysis datasets from the Indian Monsoon Data Assimilation and Analysis (IMDAA) and the European Centre for Medium-Range Weather Forecasts (ERA5) products, as well as biophysical measurements from the MODIS satellite. An analysis of the annual water budget, based on ERA5 precipitation data, highlights net positive water balances across the study sites. By harnessing diverse datasets, we employ various machine learning regression algorithms. We find the support vector regression superior to linear, lasso, random forest, adaptive boosting and gradient boosting algorithms. This study highlights the robustness of support vector regression and accentuates the impact of climatic and environmental conditions on model performance, ultimately contributing to more precise predictions of latent heat flux.</p>","PeriodicalId":49825,"journal":{"name":"Meteorological Applications","volume":"32 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/met.70023","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143112751","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unveiling meteorological synergies in the coupling of an abnormal easterly wave and cutoff low in South Africa's February 2023 rainfall","authors":"Farahnaz Fazel-Rastgar,&nbsp;Venkataraman Sivakumar,&nbsp;Masoud Rostami,&nbsp;Bijan Fallah","doi":"10.1002/met.70027","DOIUrl":"https://doi.org/10.1002/met.70027","url":null,"abstract":"<p>This study seeks to understand the meteorological mechanisms that caused widespread and heavy rainfall from 6 to 14 February 2023, over southern Mozambique and the eastern and northeastern areas in South Africa, including Limpopo Province, Mpumalanga Province and northern KwaZulu-Natal, by examining different outputs from reanalysis datasets. The heavy rainfall had a substantial hydrological impact, leading to significant flooding and disruptions. This research revealed that a slow-moving cutoff low (COL) system remained over the central parts of South Africa, triggering extensive and heavy rainfall mostly over the northeastern and eastern provinces. The outcomes from the reanalysis datasets display the influence of the weather system and the interaction between an initiated westerly wave, which converted into a near-stationary upper-air cold core upper air COL system, and the easterly wind wave associated with the South Indian Ocean Convergence Zone (SICZ), bringing significant warm humid air from the Indian Ocean into the study area. This study revealed an abnormal structural pattern in the wind vectors, low-pressure trough, upper and mid-tropospheric westerly flows and humidity compared with the long-term climate normal values over Mozambique and the northeastern and eastern regions of southern Africa. This event is exciting from a meteorological perspective due to its intensity and duration, the involvement of cyclonic activity and its implications for understanding the impacts of climate change on weather patterns in southern Africa. The heavy rainfall had a substantial hydrological impact, leading to significant flooding and disruptions, providing valuable data for improving forecasting models and disaster preparedness strategies and underscoring the importance of enhancing climate resilience in regions prone to extreme weather.</p>","PeriodicalId":49825,"journal":{"name":"Meteorological Applications","volume":"32 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/met.70027","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143113019","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Estimation of extreme wind speeds with different return periods in the Northwest Pacific","authors":"Lisha Kong,&nbsp;Xiuzhi Zhang,&nbsp;Huanping Wu,&nbsp;Yu Li","doi":"10.1002/met.70012","DOIUrl":"https://doi.org/10.1002/met.70012","url":null,"abstract":"<p>It is vital to analyze extreme wind speed in marine engineering designs. However, due to the lack of observational data, it is impossible to establish the measured long-term wind speed series. This study simulates the annual hourly wind field of every tropical cyclone (TC) with a resolution of 5 km in the Northwest Pacific (NWP) from 1981 to 2020. On this basis, combined with the sea surface wind speed data observed by the satellites and the ships, the 40-year annual maximum wind speed series of NWP are established. The Gumbel, three-parameter Weibull (Weibull-3par), two-parameter Weibull (Weibull-2par), generalized extreme-value (GEV) distribution, and the two parameter estimation methods are used to estimate the extreme wind speeds with different return periods (RPs) at four typical locations in the NWP. Meanwhile, the effects of different extreme-value distributions and different parameter estimation methods on the estimation results are discussed. Subsequently, the best distribution and parameter estimation method for each grid in the NWP are determined by the goodness-of-fit test, and then the spatial distributions of extreme wind speeds with different RPs along with uncertainty estimates in the entire NWP are obtained. The results show that extreme wind speeds with RPs of 5, 25, 50, and 100 years in the east of Taiwan and Philippines can reach a maximum of 43.8, 60.8, 70.4, and 81.4 m s⁻¹, respectively.</p>","PeriodicalId":49825,"journal":{"name":"Meteorological Applications","volume":"31 6","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/met.70012","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142851413","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact of INSAT-3D land surface temperature assimilation via simplified extended Kalman filter-based land data assimilation system on forecasting of surface fields over India","authors":"Abhishek Lodh,&nbsp;Ashish Routray,&nbsp;Devajyoti Dutta,&nbsp;Vivek Singh,&nbsp;John. P. George","doi":"10.1002/met.70019","DOIUrl":"https://doi.org/10.1002/met.70019","url":null,"abstract":"<p>The land surface temperature (LT) is a crucial variable that governs the energy and radiation budget of the earth's atmosphere and influences land-atmosphere interactions. The LT plays a crucial role mainly in the short-range forecast of a numerical weather prediction (NWP) model. The primary research goal in this research work undertaken is to assess the impact of assimilation of LT data from the Indian satellite (INSAT-3D) into the NCMRWF global NWP model (NCUM) through a simplified Extended Kalman Filter (sEKF) land data assimilation system (LDAS), particularly important as there are few screen-level observations over the region. A dedicated stand-alone pre-processing system has been designed to prepare LT observations in a compatible format for the land surface assimilation system. The approach for LT data assimilation from the INSAT-3D satellite reduces the uncertainty associated with the initial state of LT analysis while simultaneously improving the accuracy of forecasts of near surface atmospheric variables. An observing system experiment (OSE) was carried out during both the summer (May) and winter (February) months by assimilating the INSAT-3D LT data in a coupled land-atmosphere analysis-forecast system. The results obtained from the OSE demonstrate that the use of INSAT-3D LT data improves the forecast skill of both maximum and minimum temperature over India, particularly in areas characterized by higher LT variability. The improvement is pronounced in forecasts of maximum (minimum) temperature during “Boreal” summer (“Boreal” winter) season. The verification scores also indicate that the incorporation of INSAT LT data substantially improves the NCUM model's forecast performance. By assimilating LT, the mean error of maximum and minimum temperature forecasts in India was decreased, accompanied by enhanced forecast accuracy within a time frame of approximately 24 h. The scores for the verification measures, specifically the Probability of Detection (POD), demonstrate a ~15% improvement in both the forecasts for maximum and minimum temperatures. This improves the temperature prediction as well as the ability to forecast intense weather episodes like cold spells and heat waves.</p>","PeriodicalId":49825,"journal":{"name":"Meteorological Applications","volume":"31 6","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/met.70019","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142860970","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improving blended probability forecasts with neural networks","authors":"Belinda Trotta","doi":"10.1002/met.70021","DOIUrl":"https://doi.org/10.1002/met.70021","url":null,"abstract":"<p>Operational forecasting systems often combine calibrated probabilistic outputs from several numerical weather prediction (NWP) models. A common approach is to use a weighted blend, with the more accurate models having higher weights. We show that this approach is not ideal and that using a simple neural network to combine forecasts yields better results. The sharpness of the forecast is increased, so that extreme events are more likely to be predicted. Improvements are also observed in accuracy as measured by the continuous rank probability score (CRPS) and reliability. The proposed neural network model has a simple architecture with few parameters, and training and inference can easily be done using a central processing unit. This makes it a practical option for improving the accuracy of blended operational forecasts.</p>","PeriodicalId":49825,"journal":{"name":"Meteorological Applications","volume":"31 6","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/met.70021","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142860971","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction to “Skilful probabilistic medium-range precipitation and temperature forecasts over Vietnam for the development of a future dengue early warning system”","authors":"","doi":"10.1002/met.70018","DOIUrl":"https://doi.org/10.1002/met.70018","url":null,"abstract":"<p>Main, L., Sparrow, S., Weisheimer, A., &amp; Wright, M. (2024) Skilful probabilistic medium-range precipitation and temperature forecasts over Vietnam for the development of a future dengue early warning system. <i>Meteorological Applications</i>, 31(4), e2222. Available from: https://doi.org/10.1002/met.2222</p><p>We apologize for this error.</p>","PeriodicalId":49825,"journal":{"name":"Meteorological Applications","volume":"31 6","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/met.70018","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142851276","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Drought forecasting with regionalization of climate variables and generalized linear model","authors":"Taesam Lee,&nbsp;Yejin Kong,&nbsp;Joo-Heon Lee,&nbsp;Chang-Hee Won","doi":"10.1002/met.70016","DOIUrl":"https://doi.org/10.1002/met.70016","url":null,"abstract":"<p>Spring drought forecasting is essential in South Korea for managing water resources reliably and cultivating agricultural products efficiently, as seasonal rainfall difference often drives water shortage during spring. In the current study, a novel scheme for spring drought forecasting was suggested by extensively searching appropriate predictors from the global climate variable: here mean sea level pressure (MSLP) of the winter season due to its time lag for forecasting. The target series was estimated with the median of the spring precipitation series of the weather stations over South Korea, called the accumulated spring precipitation (ASP). A number of points of the MSLP data were detected as significant cross-correlation with the ASP and also the points were regionally grouped. Therefore, the regionalization for the high correlation points was performed, resulting in three regions, such as Arctic Ocean (R1), South Pacific (R2), and South Africa (R3). The R1 and R2 regions are located at the places where climate indices have been developed such as Arctic Oscillation and North Atlantic Oscillation for R1 and the indicator of El-Nino and Southern Oscillation for R2. The generalized linear model (GLM) was adopted in ASP drought forecasting with the driven three regionalized indices as the predictors of the ASP. The result indicates that the regionalized indices can produce a good performance in forecasting the ASP. The forecasting result can be employed as a good tool for managing water resources and planning better cultivation in agriculture industries.</p>","PeriodicalId":49825,"journal":{"name":"Meteorological Applications","volume":"31 6","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/met.70016","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142763988","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Numerical simulation and its optimization of cold air pools in the Lanzhou Valley","authors":"Minjin Ma,&nbsp;Guoqiang Kang,&nbsp;Zhenzhu Zhao,&nbsp;Yidan Cao","doi":"10.1002/met.70020","DOIUrl":"https://doi.org/10.1002/met.70020","url":null,"abstract":"<p>Persistent cold air pools (CAPs) trap pollutants in valleys for extended periods, leading to reduced visibility and increased air pollution within these valleys. The structure of the persistent cold air pool that occurred in the Lanzhou Valley in December 2016 was simulated using different Planetary Boundary Layer (PBL) scenarios of the Weather Research and Forecasting (WRF) model, and the simulation of the persistent cold air pool was further optimized in these PBL scenarios. The simulation results indicated that weather-scale dry subsidence and nighttime ground radiation cooling were significant factors contributing to the accumulation of persistent CAPs and pollutants in the Lanzhou Valley. In contrast, convective lifting from the ground led to the dissipation of persistent CAPs and a reduction in pollution within the valley. During persistent CAPs, the PM_2.5 concentration and valley heat deficit (Q) were 66.7% and 62% higher, respectively, than during non-CAP. In the original MYNN scheme, the average PBL height, double turbulent kinetic energy (QKE), and turbulence length scale during persistent CAPs decreased by 30.79%, 50.5%, and 34.4%, respectively, compared to non-CAP. Compared with the original MYNN scheme, the optimized MYNN scheme shows a significant improvement in the turbulence simulation during the sustained CAPs, resulting in a more stable atmosphere. The PBL height during the sustained CAPs is reduced by 28 m, the diurnal turbulence length scale is reduced by 31.62%, the stability parameter is reduced by 39%, the diurnal mean QKE is reduced by 27.45%, and the QKE impact height is reduced by 100–400 m.</p>","PeriodicalId":49825,"journal":{"name":"Meteorological Applications","volume":"31 6","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/met.70020","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142749138","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}