Since the efficiency of photovoltaic(PV) power is closely related to the weather,many PV enterprises install weather instruments to monitor the working state of the PV power system.With the development of the soft mea...Since the efficiency of photovoltaic(PV) power is closely related to the weather,many PV enterprises install weather instruments to monitor the working state of the PV power system.With the development of the soft measurement technology,the instrumental method seems obsolete and involves high cost.This paper proposes a novel method for predicting the types of weather based on the PV power data and partial meteorological data.By this method,the weather types are deduced by data analysis,instead of weather instrument A better fault detection is obtained by using the support vector machines(SVM) and comparing the predicted and the actual weather.The model of the weather prediction is established by a direct SVM for training multiclass predictors.Although SVM is suitable for classification,the classified results depend on the type of the kernel,the parameters of the kernel,and the soft margin coefficient,which are difficult to choose.In this paper,these parameters are optimized by particle swarm optimization(PSO) algorithm in anticipation of good prediction results can be achieved.Prediction results show that this method is feasible and effective.展开更多
A new forecasting system-the System of Multigrid Nonlinear Least-squares Four-dimensional Variational(NLS-4DVar)Data Assimilation for Numerical Weather Prediction(SNAP)-was established by building upon the multigrid N...A new forecasting system-the System of Multigrid Nonlinear Least-squares Four-dimensional Variational(NLS-4DVar)Data Assimilation for Numerical Weather Prediction(SNAP)-was established by building upon the multigrid NLS-4DVar data assimilation scheme,the operational Gridpoint Statistical Interpolation(GSI)−based data-processing and observation operators,and the widely used Weather Research and Forecasting numerical model.Drawing upon lessons learned from the superiority of the operational GSI analysis system,for its various observation operators and the ability to assimilate multiple-source observations,SNAP adopts GSI-based data-processing and observation operator modules to compute the observation innovations.The multigrid NLS-4DVar assimilation framework is used for the analysis,which can adequately correct errors from large to small scales and accelerate iteration solutions.The analysis variables are model state variables,rather than the control variables adopted in the conventional 4DVar system.Currently,we have achieved the assimilation of conventional observations,and we will continue to improve the assimilation of radar and satellite observations in the future.SNAP was evaluated by case evaluation experiments and one-week cycling assimilation experiments.In the case evaluation experiments,two six-hour time windows were established for assimilation experiments and precipitation forecasts were verified against hourly precipitation observations from more than 2400 national observation sites.This showed that SNAP can absorb observations and improve the initial field,thereby improving the precipitation forecast.In the one-week cycling assimilation experiments,six-hourly assimilation cycles were run in one week.SNAP produced slightly lower forecast RMSEs than the GSI 4DEnVar(Four-dimensional Ensemble Variational)as a whole and the threat scores of precipitation forecasts initialized from the analysis of SNAP were higher than those obtained from the analysis of GSI 4DEnVar.展开更多
Numerical Weather Prediction(NWP)is a necessary input for short-term wind power forecasting.Existing NWP models are all based on purely physical models.This requires mainframe computers to perform large-scale numerica...Numerical Weather Prediction(NWP)is a necessary input for short-term wind power forecasting.Existing NWP models are all based on purely physical models.This requires mainframe computers to perform large-scale numerical calculations and the technical threshold of the assimilation process is high.There is a need to further improve the timeliness and accuracy of the assimilation process.In order to solve the above problems,NWP method based on artificial intelligence is proposed in this paper.It uses a convolutional neural network algorithm and a downscaling model from the global background field to establish a given wind turbine hub height position.We considered the actual data of a wind farm in north China as an example to analyze the calculation example.The results show that the prediction accuracy of the proposed method is equivalent to that of the traditional purely physical model.The prediction accuracy in some months is better than that of the purely physical model,and the calculation efficiency is considerably improved.The validity and advantages of the proposed method are verified from the results,and the traditional NWP method is replaced to a certain extent.展开更多
Many weather radar networks in the world have now provided polarimetric radar data(PRD)that have the potential to improve our understanding of cloud and precipitation microphysics,and numerical weather prediction(NWP)...Many weather radar networks in the world have now provided polarimetric radar data(PRD)that have the potential to improve our understanding of cloud and precipitation microphysics,and numerical weather prediction(NWP).To realize this potential,an accurate and efficient set of polarimetric observation operators are needed to simulate and assimilate the PRD with an NWP model for an accurate analysis of the model state variables.For this purpose,a set of parameterized observation operators are developed to simulate and assimilate polarimetric radar data from NWP model-predicted hydrometeor mixing ratios and number concentrations of rain,snow,hail,and graupel.The polarimetric radar variables are calculated based on the T-matrix calculation of wave scattering and integrations of the scattering weighted by the particle size distribution.The calculated polarimetric variables are then fitted to simple functions of water content and volumeweighted mean diameter of the hydrometeor particle size distribution.The parameterized PRD operators are applied to an ideal case and a real case predicted by the Weather Research and Forecasting(WRF)model to have simulated PRD,which are compared with existing operators and real observations to show their validity and applicability.The new PRD operators use less than one percent of the computing time of the old operators to complete the same simulations,making it efficient in PRD simulation and assimilation usage.展开更多
Although the first successful numerical weather prediction(NWP)project led by Charney and von Neumann is widely known,little is known by the international community about the development of NWP during the 1950s in Chi...Although the first successful numerical weather prediction(NWP)project led by Charney and von Neumann is widely known,little is known by the international community about the development of NWP during the 1950s in China.Here,a detailed historical perspective on the early NWP experiments in China is provided.The leadership in NWP of the late Professor Chen-Chao Koo,a protége of C.G.Rossby at the University of Stockholm during the late 1940s and a key leader of modern meteorology(particularly of atmospheric dynamics and physics)in China during the 1950s−70s,is highlighted.The unique contributions to NWP by Koo and his students,such as the ideas of formulating NWP as an“evolution”problem,in which the past data over multiple time steps are utilized,rather than an initial-value problem,and on the cybernetic aspects of atmospheric processes,i.e.,regarding the motion of the atmosphere at various time scales as an optimal control system,are also emphasized.展开更多
After decades of research and development, the WSR-88 D(NEXRAD) network in the United States was upgraded with dual-polarization capability, providing polarimetric radar data(PRD) that have the potential to improve we...After decades of research and development, the WSR-88 D(NEXRAD) network in the United States was upgraded with dual-polarization capability, providing polarimetric radar data(PRD) that have the potential to improve weather observations,quantification, forecasting, and warnings. The weather radar networks in China and other countries are also being upgraded with dual-polarization capability. Now, with radar polarimetry technology having matured, and PRD available both nationally and globally, it is important to understand the current status and future challenges and opportunities. The potential impact of PRD has been limited by their oftentimes subjective and empirical use. More importantly, the community has not begun to regularly derive from PRD the state parameters, such as water mixing ratios and number concentrations, used in numerical weather prediction(NWP) models.In this review, we summarize the current status of weather radar polarimetry, discuss the issues and limitations of PRD usage, and explore potential approaches to more efficiently use PRD for quantitative precipitation estimation and forecasting based on statistical retrieval with physical constraints where prior information is used and observation error is included. This approach aligns the observation-based retrievals favored by the radar meteorology community with the model-based analysis of the NWP community. We also examine the challenges and opportunities of polarimetric phased array radar research and development for future weather observation.展开更多
In this report, we summarize the needs of space weather models, and recommend that developing operational prediction models, rather than transitioning from research to operation, is a more feasible and critical way fo...In this report, we summarize the needs of space weather models, and recommend that developing operational prediction models, rather than transitioning from research to operation, is a more feasible and critical way for space weather services in the near future. Operational models for solar wind speed, geomagnetic indices, magnetopause, plasma sheet energetic electrons, inner boundary of ion plasma sheet, energetic electrons in outer radiation belt, and thermospheric density at low Earth orbit, have been developed and will be introduced briefly here. Their applications made a big progress in space weather services during the past two years in China.展开更多
The recent progresses in the research and development of (NWP) in China are reviewed in this paper. The most impressive achievements are the development of direct assimilation of satellite irradiances with a 3DVAR (th...The recent progresses in the research and development of (NWP) in China are reviewed in this paper. The most impressive achievements are the development of direct assimilation of satellite irradiances with a 3DVAR (three-dimentional variational) data assimilation system and a non-hydrostatic model with a semi-Lagrangian semi-implicit scheme. Progresses have also been made in model physics and model application to precipitation and environmental forecasts. Some scientific issues of great importance for further development are discussed.展开更多
The initial value error and the imperfect numerical model are usually considered as error sources of numerical weather prediction (NWP). By using past multi-time observations and model output, this study proposes a me...The initial value error and the imperfect numerical model are usually considered as error sources of numerical weather prediction (NWP). By using past multi-time observations and model output, this study proposes a method to estimate imperfect numerical model error. This method can be inversely estimated through expressing the model error as a Lagrange interpolation polynomial, while the coefficients of polynomial are determined by past model performance. However, for practical application in the full NWP model, it is necessary to determine the following criteria: (1) the length of past data sufficient for estimation of the model errors, (2) a proper method of estimating the term "model integration with the exact solution" when solving the inverse problem, and (3) the extent to which this scheme is sensitive to the observational errors. In this study, such issues are resolved using a simple linear model, and an advection-diffusion model is applied to discuss the sensitivity of the method to an artificial error source. The results indicate that the forecast errors can be largely reduced using the proposed method if the proper length of past data is chosen. To address the three problems, it is determined that (1) a few data limited by the order of the corrector can be used, (2) trapezoidal approximation can be employed to estimate the "term" in this study; however, a more accurate method should be explored for an operational NWP model, and (3) the correction is sensitive to observational error.展开更多
Model error is one of the key factors restricting the accuracy of numerical weather prediction(NWP). Considering the continuous evolution of the atmosphere, the observed data(ignoring the measurement error) can be vie...Model error is one of the key factors restricting the accuracy of numerical weather prediction(NWP). Considering the continuous evolution of the atmosphere, the observed data(ignoring the measurement error) can be viewed as a series of solutions of an accurate model governing the actual atmosphere. Model error is represented as an unknown term in the accurate model, thus NWP can be considered as an inverse problem to uncover the unknown error term. The inverse problem models can absorb long periods of observed data to generate model error correction procedures. They thus resolve the deficiency and faultiness of the NWP schemes employing only the initial-time data. In this study we construct two inverse problem models to estimate and extrapolate the time-varying and spatial-varying model errors in both the historical and forecast periods by using recent observations and analogue phenomena of the atmosphere. Numerical experiment on Burgers' equation has illustrated the substantial forecast improvement using inverse problem algorithms. The proposed inverse problem methods of suppressing NWP errors will be useful in future high accuracy applications of NWP.展开更多
Based on the real case of a frontal precipitation process affecting South China, 27 controlled numerical experiments was made for the effects of hydrostatic and non-hydrostatic effects, different driving models, combi...Based on the real case of a frontal precipitation process affecting South China, 27 controlled numerical experiments was made for the effects of hydrostatic and non-hydrostatic effects, different driving models, combinations of initial/boundary conditions, updates of lateral values and initial time levels of forecast, on model predictions. Features about the impact of initial/boundary conditions on mesoscale numerical weather prediction (NWP) model are analyzed and discussed in detail. Some theoretically and practically valuable conclusions are drawn. It is found that the overall tendency of mesoscale NWP models is governed by its driving model, with the initial conditions showing remarkable impacts on mesoscale models for the first I0 hours of the predictions while leaving lateral boundary conditions to take care the period beyond; the latter affect the inner area of mesoscale predictions mainly through the propagation and movement of weather signals (waves) of different time scales; initial values of external model parameters such as soil moisture content may affect predictions of more longer time validity, while fast signals may be filtered away and only information with time scale 4 times as large as or more than the updated period of boundary values may be introduced, through lateral boundary, to mesoseale models, etc. Some results may be taken as important guidance on mesoseale model and its data a.ssimilation developments of the future.展开更多
This paper is based on the results of a pilot project conducted to strengthen Nigerian Meteorological Agency’s (NIMET) capacity to provide reliable planting date forecast in Nigeria. This aspect of the project aimed ...This paper is based on the results of a pilot project conducted to strengthen Nigerian Meteorological Agency’s (NIMET) capacity to provide reliable planting date forecast in Nigeria. This aspect of the project aimed at understanding traditional knowledge base and farmers’ prediction methods, community perceptions of impacts of rainfall variability, coping strategies and opportunities in Sokoto, Kano, Jigawa, Kaduna, Bauchi states of Nigeria. Based on prevalence of drought, a community was selected for survey in each of the five states. Semi-structured interview and focus group discussion were used to sources for information. The survey indicates that the farmers had good understanding of weather and climatic dynamics of their community. The farmers in the study locations characterize a year into five seasons based on the atmospheric temperature as felt by the body, changes in wind direction, farming activities, and the behavioral changes of some animal and birds and phenological changes in plant species. Rainfall variability in the community has altered the farming systems, either in terms of changes in cropping pattern, elimination/reduction in the level of producing some crops or introduction of new crop varieties that are drought resistant and early maturing, and diversification of source of livelihood (non-farm activities). Impacts of rainfall variability in the communities were asserted to include;poor yield, low prices of crop/livestock, low dowry for their daughters, high cost of labor as a result of migration to urban centers, inadequate water for dry season farming, low income, low standard of living, and high level of poverty. Farmers recommended an integration of traditional proven methods of rainfall prediction with scientific methods to evolve reliable forecast that will reduce risks in their rainfed farming systems.展开更多
With the development of space exploration and space environment measurements,the numerous observations of solar,solar wind,and near Earth space environment have been obtained in last 20 years.The accumulation of multi...With the development of space exploration and space environment measurements,the numerous observations of solar,solar wind,and near Earth space environment have been obtained in last 20 years.The accumulation of multiple data makes it possible to better use machine learning technique,which has achieved unforeseen results in industrial applications in last decades,for developing new approaches and models in space weather investigation and prediction.In this paper,the efforts on the forecasting methods for space weather indices,events,and parameters using machine learning are briefly introduced based on the study works in recent years.These investigations indicate that machine learning,especially deep learning technique can be used in automatic characteristic identification,solar eruption prediction,space weather forecasting for solar and geomagnetic indices,and modeling of space environment parameters.展开更多
The TOVS data are used to study the structure of a number of tropical cyclones for the year 2000. Differences are found to some extent between what is found and classic conceptual models in that (1) the horizontal str...The TOVS data are used to study the structure of a number of tropical cyclones for the year 2000. Differences are found to some extent between what is found and classic conceptual models in that (1) the horizontal structure is asymmetric and variable so that the low-value centers at low levels of the geopotential height field (or the high-value centers at high levels) do not necessarily coincide with the high-value centers of the temperature field; (2) the vertical structure is also variable in the allocation of the anomalies of the geopotential height field between low values at low levels and high values at high levels. It is especially noted that the centers of the anomalies are tilting at both high and low levels or the high level is only at the edge of a high-pressure zone. There is not any significant high-value anomalous center in a corresponding location with the tropical cyclone. The structure of tropical cyclone in the TOVS is also used as reference to modify the structure of typhoon BOGUS in the numerical prediction model system of tropical cyclones. It is found that the modified BOGUS performs better in coordinating with the environment and predicting the track of the tropical cyclone. The demonstration is two-fold the structure of the typhoon BOGUS is such that it means much in the track prediction and the use of the TOVS-based tropical cyclone structure really helps in improving it. It provides the foundation for modification and evolution of typhoon BOGUS.展开更多
This paper presents the development of numerical prediction products(NPP) correction and display system(NPPCDS) for rapid and effective post-processing and displaying of the T213 NPP(numerical prediction products of t...This paper presents the development of numerical prediction products(NPP) correction and display system(NPPCDS) for rapid and effective post-processing and displaying of the T213 NPP(numerical prediction products of the medium range numerical weather prediction spectral model T213L31) through instant correction method. The NPPCDS consists of two modules: an automatic correction module and a graphical display module. The automatic correction module automatically corrects the T213 NPP at regularly scheduled time intervals, while the graphical display module interacts with users to display the T213 NPP and its correction results. The system helps forecasters extract the most relevant information at a quick glance without extensive post-processing. It is simple, easy to use, and computationally efficient, and has been running stably at Huludao Meteorological Bureau in Liaoning Province of China for the past three years. Because of its low computational costs, it is particularly useful for meteorological departments that lack advanced computing capacity and still need to make short-range weather forecasting.展开更多
Numerical Weather Prediction(NWP)simulations produce meteorological data in various spatial and temporal scales,depending on the application requirements.In the current study,a deep learning approach,based on convolut...Numerical Weather Prediction(NWP)simulations produce meteorological data in various spatial and temporal scales,depending on the application requirements.In the current study,a deep learning approach,based on convolutional autoencoders,is explored to effectively correct the error of the NWP simulation.An undercomplete convolutional autoencoder(CAE)is applied as part of the dynamic error correction of NWP data.This work is an attempt to improve the seasonal forecast(3-6 months ahead)data accuracy for Greece using a global reanalysis dataset(that incorporates observations,satellite imaging,etc.)of higher spatial resolution.More specifically,the publically available Meteo France Seasonal(Copernicus platform)and the National Centers for Environmental Prediction(NCEP)Final Analysis(FNL)(NOAA)datasets are utilized.In addition,external information is used as evidence transfer,concerning the time conditions(month,day,and season)and the simulation characteristics(initialization of simulation).It is found that convolutional autoencoders help to improve the resolution of the seasonal data and successfully reduce the error of the NWP data for 6-months ahead forecasting.Interestingly,the month evidence yields the best agreement indicating a seasonal dependence of the performance.展开更多
The aim of this article is to assist farmers in making better crop selection decisions based on soil fertility and weather forecast through the use of IoT and AI (smart farming). To accomplish this, a prototype was de...The aim of this article is to assist farmers in making better crop selection decisions based on soil fertility and weather forecast through the use of IoT and AI (smart farming). To accomplish this, a prototype was developed capable of predicting the best suitable crop for a specific plot of land based on soil fertility and making recommendations based on weather forecast. Random Forest machine learning algorithm was used and trained with Jupyter in the Anaconda framework to achieve an accuracy of about 99%. Based on this process, IoT with the Message Queuing Telemetry Transport (MQTT) protocol, a machine learning algorithm, based on Random Forest, and weather forecast API for crop prediction and recommendations were used. The prototype accepts nitrogen, phosphorus, potassium, humidity, temperature and pH as input parameters from the IoT sensors, as well as the weather API for data forecasting. The approach was tested in a suburban area of Yaounde (Cameroon). Taking into account future meteorological parameters (rainfall, wind and temperature) in this project produced better recommendations and therefore better crop selection. All necessary results can be accessed from anywhere and at any time using the IoT system via a web browser.展开更多
基金supported by the National Natural Science Foundation of China(61433004,61473069)IAPI Fundamental Research Funds(2013ZCX14)+1 种基金supported by the Development Project of Key Laboratory of Liaoning Provincethe Enterprise Postdoctoral Fund Projects of Liaoning Province
文摘Since the efficiency of photovoltaic(PV) power is closely related to the weather,many PV enterprises install weather instruments to monitor the working state of the PV power system.With the development of the soft measurement technology,the instrumental method seems obsolete and involves high cost.This paper proposes a novel method for predicting the types of weather based on the PV power data and partial meteorological data.By this method,the weather types are deduced by data analysis,instead of weather instrument A better fault detection is obtained by using the support vector machines(SVM) and comparing the predicted and the actual weather.The model of the weather prediction is established by a direct SVM for training multiclass predictors.Although SVM is suitable for classification,the classified results depend on the type of the kernel,the parameters of the kernel,and the soft margin coefficient,which are difficult to choose.In this paper,these parameters are optimized by particle swarm optimization(PSO) algorithm in anticipation of good prediction results can be achieved.Prediction results show that this method is feasible and effective.
基金the National Key Research and Development Program of China(Grant No.2016YFA0600203)the National Natural Science Foundation of China(Grant No.41575100)+1 种基金the Key Research Program of Frontier Sciences,Chinese Academy of Sciences(Grant No.QYZDY-SSW-DQC012)the CMA Special Public Welfare Research Fund(Grant No.GYHY201506002).
文摘A new forecasting system-the System of Multigrid Nonlinear Least-squares Four-dimensional Variational(NLS-4DVar)Data Assimilation for Numerical Weather Prediction(SNAP)-was established by building upon the multigrid NLS-4DVar data assimilation scheme,the operational Gridpoint Statistical Interpolation(GSI)−based data-processing and observation operators,and the widely used Weather Research and Forecasting numerical model.Drawing upon lessons learned from the superiority of the operational GSI analysis system,for its various observation operators and the ability to assimilate multiple-source observations,SNAP adopts GSI-based data-processing and observation operator modules to compute the observation innovations.The multigrid NLS-4DVar assimilation framework is used for the analysis,which can adequately correct errors from large to small scales and accelerate iteration solutions.The analysis variables are model state variables,rather than the control variables adopted in the conventional 4DVar system.Currently,we have achieved the assimilation of conventional observations,and we will continue to improve the assimilation of radar and satellite observations in the future.SNAP was evaluated by case evaluation experiments and one-week cycling assimilation experiments.In the case evaluation experiments,two six-hour time windows were established for assimilation experiments and precipitation forecasts were verified against hourly precipitation observations from more than 2400 national observation sites.This showed that SNAP can absorb observations and improve the initial field,thereby improving the precipitation forecast.In the one-week cycling assimilation experiments,six-hourly assimilation cycles were run in one week.SNAP produced slightly lower forecast RMSEs than the GSI 4DEnVar(Four-dimensional Ensemble Variational)as a whole and the threat scores of precipitation forecasts initialized from the analysis of SNAP were higher than those obtained from the analysis of GSI 4DEnVar.
基金supported by the Science and Technology Project of State Grid Corporation of China:Key technology for high-resolution and centralized wind power forecasting for deep-offshore wind power base (No. SGSXDK00YJJS2000879)
文摘Numerical Weather Prediction(NWP)is a necessary input for short-term wind power forecasting.Existing NWP models are all based on purely physical models.This requires mainframe computers to perform large-scale numerical calculations and the technical threshold of the assimilation process is high.There is a need to further improve the timeliness and accuracy of the assimilation process.In order to solve the above problems,NWP method based on artificial intelligence is proposed in this paper.It uses a convolutional neural network algorithm and a downscaling model from the global background field to establish a given wind turbine hub height position.We considered the actual data of a wind farm in north China as an example to analyze the calculation example.The results show that the prediction accuracy of the proposed method is equivalent to that of the traditional purely physical model.The prediction accuracy in some months is better than that of the purely physical model,and the calculation efficiency is considerably improved.The validity and advantages of the proposed method are verified from the results,and the traditional NWP method is replaced to a certain extent.
基金the University of Oklahoma(OU)Supercomputing Center for Education&Research(OSCER).
文摘Many weather radar networks in the world have now provided polarimetric radar data(PRD)that have the potential to improve our understanding of cloud and precipitation microphysics,and numerical weather prediction(NWP).To realize this potential,an accurate and efficient set of polarimetric observation operators are needed to simulate and assimilate the PRD with an NWP model for an accurate analysis of the model state variables.For this purpose,a set of parameterized observation operators are developed to simulate and assimilate polarimetric radar data from NWP model-predicted hydrometeor mixing ratios and number concentrations of rain,snow,hail,and graupel.The polarimetric radar variables are calculated based on the T-matrix calculation of wave scattering and integrations of the scattering weighted by the particle size distribution.The calculated polarimetric variables are then fitted to simple functions of water content and volumeweighted mean diameter of the hydrometeor particle size distribution.The parameterized PRD operators are applied to an ideal case and a real case predicted by the Weather Research and Forecasting(WRF)model to have simulated PRD,which are compared with existing operators and real observations to show their validity and applicability.The new PRD operators use less than one percent of the computing time of the old operators to complete the same simulations,making it efficient in PRD simulation and assimilation usage.
基金the National Natural Science Foundation of China(Grant No.42042011)is appreciated.
文摘Although the first successful numerical weather prediction(NWP)project led by Charney and von Neumann is widely known,little is known by the international community about the development of NWP during the 1950s in China.Here,a detailed historical perspective on the early NWP experiments in China is provided.The leadership in NWP of the late Professor Chen-Chao Koo,a protége of C.G.Rossby at the University of Stockholm during the late 1940s and a key leader of modern meteorology(particularly of atmospheric dynamics and physics)in China during the 1950s−70s,is highlighted.The unique contributions to NWP by Koo and his students,such as the ideas of formulating NWP as an“evolution”problem,in which the past data over multiple time steps are utilized,rather than an initial-value problem,and on the cybernetic aspects of atmospheric processes,i.e.,regarding the motion of the atmosphere at various time scales as an optimal control system,are also emphasized.
基金supported by the NOAA (Grant Nos. NA16AOR4320115 and NA11OAR4320072)NSF (Grant No. AGS-1341878)
文摘After decades of research and development, the WSR-88 D(NEXRAD) network in the United States was upgraded with dual-polarization capability, providing polarimetric radar data(PRD) that have the potential to improve weather observations,quantification, forecasting, and warnings. The weather radar networks in China and other countries are also being upgraded with dual-polarization capability. Now, with radar polarimetry technology having matured, and PRD available both nationally and globally, it is important to understand the current status and future challenges and opportunities. The potential impact of PRD has been limited by their oftentimes subjective and empirical use. More importantly, the community has not begun to regularly derive from PRD the state parameters, such as water mixing ratios and number concentrations, used in numerical weather prediction(NWP) models.In this review, we summarize the current status of weather radar polarimetry, discuss the issues and limitations of PRD usage, and explore potential approaches to more efficiently use PRD for quantitative precipitation estimation and forecasting based on statistical retrieval with physical constraints where prior information is used and observation error is included. This approach aligns the observation-based retrievals favored by the radar meteorology community with the model-based analysis of the NWP community. We also examine the challenges and opportunities of polarimetric phased array radar research and development for future weather observation.
文摘In this report, we summarize the needs of space weather models, and recommend that developing operational prediction models, rather than transitioning from research to operation, is a more feasible and critical way for space weather services in the near future. Operational models for solar wind speed, geomagnetic indices, magnetopause, plasma sheet energetic electrons, inner boundary of ion plasma sheet, energetic electrons in outer radiation belt, and thermospheric density at low Earth orbit, have been developed and will be introduced briefly here. Their applications made a big progress in space weather services during the past two years in China.
基金This work is jointly funded by the national key-research project ＂Innovative Researches on Chinese Numerical Weather Prediction System＂ （Grant No. 2004BA607B）the project of National Natural Science Foundation of China ＂Study on Weather Prediction Associated with Heavy Precipitation in China＂ （Grant No. 40233036）.
文摘The recent progresses in the research and development of (NWP) in China are reviewed in this paper. The most impressive achievements are the development of direct assimilation of satellite irradiances with a 3DVAR (three-dimentional variational) data assimilation system and a non-hydrostatic model with a semi-Lagrangian semi-implicit scheme. Progresses have also been made in model physics and model application to precipitation and environmental forecasts. Some scientific issues of great importance for further development are discussed.
基金Project supported by the National Natural Science Foundation of China （Grant Nos 40575036 and 40325015）.Acknowledgement The authors thank Drs Zhang Pei-Qun and Bao Ming very much for their valuable comments on the present paper.
基金funded by the Special Scientific Research Project for Public Interest (GYHY201206009)the National Key Technologies Research and Development Program (Grant No. 2012BAC22B02)+2 种基金the National Natural Science Foundation Science Fund for Creative Research Groups (Grant No.41221064)the Special Scientific Research Project for Public Interest (Grant No. GYHY201006013)the National Natural Science Foundation of China (Grant No. 41105070 )
文摘The initial value error and the imperfect numerical model are usually considered as error sources of numerical weather prediction (NWP). By using past multi-time observations and model output, this study proposes a method to estimate imperfect numerical model error. This method can be inversely estimated through expressing the model error as a Lagrange interpolation polynomial, while the coefficients of polynomial are determined by past model performance. However, for practical application in the full NWP model, it is necessary to determine the following criteria: (1) the length of past data sufficient for estimation of the model errors, (2) a proper method of estimating the term "model integration with the exact solution" when solving the inverse problem, and (3) the extent to which this scheme is sensitive to the observational errors. In this study, such issues are resolved using a simple linear model, and an advection-diffusion model is applied to discuss the sensitivity of the method to an artificial error source. The results indicate that the forecast errors can be largely reduced using the proposed method if the proper length of past data is chosen. To address the three problems, it is determined that (1) a few data limited by the order of the corrector can be used, (2) trapezoidal approximation can be employed to estimate the "term" in this study; however, a more accurate method should be explored for an operational NWP model, and (3) the correction is sensitive to observational error.
基金Project supported by the Special Scientific Research Project for Public Interest(Grant No.GYHY201206009)the Fundamental Research Funds for the Central Universities,China(Grant Nos.lzujbky-2012-13 and lzujbky-2013-11)the National Basic Research Program of China(Grant Nos.2012CB955902 and 2013CB430204)
文摘Model error is one of the key factors restricting the accuracy of numerical weather prediction(NWP). Considering the continuous evolution of the atmosphere, the observed data(ignoring the measurement error) can be viewed as a series of solutions of an accurate model governing the actual atmosphere. Model error is represented as an unknown term in the accurate model, thus NWP can be considered as an inverse problem to uncover the unknown error term. The inverse problem models can absorb long periods of observed data to generate model error correction procedures. They thus resolve the deficiency and faultiness of the NWP schemes employing only the initial-time data. In this study we construct two inverse problem models to estimate and extrapolate the time-varying and spatial-varying model errors in both the historical and forecast periods by using recent observations and analogue phenomena of the atmosphere. Numerical experiment on Burgers' equation has illustrated the substantial forecast improvement using inverse problem algorithms. The proposed inverse problem methods of suppressing NWP errors will be useful in future high accuracy applications of NWP.
基金National Project "973" (Research on Heavy Rain in China) and BMBF of Germany (WTZ- Project CHN01/106)
文摘Based on the real case of a frontal precipitation process affecting South China, 27 controlled numerical experiments was made for the effects of hydrostatic and non-hydrostatic effects, different driving models, combinations of initial/boundary conditions, updates of lateral values and initial time levels of forecast, on model predictions. Features about the impact of initial/boundary conditions on mesoscale numerical weather prediction (NWP) model are analyzed and discussed in detail. Some theoretically and practically valuable conclusions are drawn. It is found that the overall tendency of mesoscale NWP models is governed by its driving model, with the initial conditions showing remarkable impacts on mesoscale models for the first I0 hours of the predictions while leaving lateral boundary conditions to take care the period beyond; the latter affect the inner area of mesoscale predictions mainly through the propagation and movement of weather signals (waves) of different time scales; initial values of external model parameters such as soil moisture content may affect predictions of more longer time validity, while fast signals may be filtered away and only information with time scale 4 times as large as or more than the updated period of boundary values may be introduced, through lateral boundary, to mesoseale models, etc. Some results may be taken as important guidance on mesoseale model and its data a.ssimilation developments of the future.
文摘This paper is based on the results of a pilot project conducted to strengthen Nigerian Meteorological Agency’s (NIMET) capacity to provide reliable planting date forecast in Nigeria. This aspect of the project aimed at understanding traditional knowledge base and farmers’ prediction methods, community perceptions of impacts of rainfall variability, coping strategies and opportunities in Sokoto, Kano, Jigawa, Kaduna, Bauchi states of Nigeria. Based on prevalence of drought, a community was selected for survey in each of the five states. Semi-structured interview and focus group discussion were used to sources for information. The survey indicates that the farmers had good understanding of weather and climatic dynamics of their community. The farmers in the study locations characterize a year into five seasons based on the atmospheric temperature as felt by the body, changes in wind direction, farming activities, and the behavioral changes of some animal and birds and phenological changes in plant species. Rainfall variability in the community has altered the farming systems, either in terms of changes in cropping pattern, elimination/reduction in the level of producing some crops or introduction of new crop varieties that are drought resistant and early maturing, and diversification of source of livelihood (non-farm activities). Impacts of rainfall variability in the communities were asserted to include;poor yield, low prices of crop/livestock, low dowry for their daughters, high cost of labor as a result of migration to urban centers, inadequate water for dry season farming, low income, low standard of living, and high level of poverty. Farmers recommended an integration of traditional proven methods of rainfall prediction with scientific methods to evolve reliable forecast that will reduce risks in their rainfed farming systems.
基金Supported by National Natural Science Foundation of China(41574181)。
文摘With the development of space exploration and space environment measurements,the numerous observations of solar,solar wind,and near Earth space environment have been obtained in last 20 years.The accumulation of multiple data makes it possible to better use machine learning technique,which has achieved unforeseen results in industrial applications in last decades,for developing new approaches and models in space weather investigation and prediction.In this paper,the efforts on the forecasting methods for space weather indices,events,and parameters using machine learning are briefly introduced based on the study works in recent years.These investigations indicate that machine learning,especially deep learning technique can be used in automatic characteristic identification,solar eruption prediction,space weather forecasting for solar and geomagnetic indices,and modeling of space environment parameters.
基金Research on Monitoring & Forecasting Techniques for Calamities by Typhoons Making Landfall on China key project of Ministry of Sci. & Tech.(2001DIA20026) Study on Pre-warning Techniques for Calamities caused by Landfalls of South China Sea Tropical Cyc
文摘The TOVS data are used to study the structure of a number of tropical cyclones for the year 2000. Differences are found to some extent between what is found and classic conceptual models in that (1) the horizontal structure is asymmetric and variable so that the low-value centers at low levels of the geopotential height field (or the high-value centers at high levels) do not necessarily coincide with the high-value centers of the temperature field; (2) the vertical structure is also variable in the allocation of the anomalies of the geopotential height field between low values at low levels and high values at high levels. It is especially noted that the centers of the anomalies are tilting at both high and low levels or the high level is only at the edge of a high-pressure zone. There is not any significant high-value anomalous center in a corresponding location with the tropical cyclone. The structure of tropical cyclone in the TOVS is also used as reference to modify the structure of typhoon BOGUS in the numerical prediction model system of tropical cyclones. It is found that the modified BOGUS performs better in coordinating with the environment and predicting the track of the tropical cyclone. The demonstration is two-fold the structure of the typhoon BOGUS is such that it means much in the track prediction and the use of the TOVS-based tropical cyclone structure really helps in improving it. It provides the foundation for modification and evolution of typhoon BOGUS.
基金Under the auspices of National Natural Science Foundation of China(No.91125010)
文摘This paper presents the development of numerical prediction products(NPP) correction and display system(NPPCDS) for rapid and effective post-processing and displaying of the T213 NPP(numerical prediction products of the medium range numerical weather prediction spectral model T213L31) through instant correction method. The NPPCDS consists of two modules: an automatic correction module and a graphical display module. The automatic correction module automatically corrects the T213 NPP at regularly scheduled time intervals, while the graphical display module interacts with users to display the T213 NPP and its correction results. The system helps forecasters extract the most relevant information at a quick glance without extensive post-processing. It is simple, easy to use, and computationally efficient, and has been running stably at Huludao Meteorological Bureau in Liaoning Province of China for the past three years. Because of its low computational costs, it is particularly useful for meteorological departments that lack advanced computing capacity and still need to make short-range weather forecasting.
基金the data provision by Copernicus platform and the National Oceanic and Atmospheric Administration(NOAA).
文摘Numerical Weather Prediction(NWP)simulations produce meteorological data in various spatial and temporal scales,depending on the application requirements.In the current study,a deep learning approach,based on convolutional autoencoders,is explored to effectively correct the error of the NWP simulation.An undercomplete convolutional autoencoder(CAE)is applied as part of the dynamic error correction of NWP data.This work is an attempt to improve the seasonal forecast(3-6 months ahead)data accuracy for Greece using a global reanalysis dataset(that incorporates observations,satellite imaging,etc.)of higher spatial resolution.More specifically,the publically available Meteo France Seasonal(Copernicus platform)and the National Centers for Environmental Prediction(NCEP)Final Analysis(FNL)(NOAA)datasets are utilized.In addition,external information is used as evidence transfer,concerning the time conditions(month,day,and season)and the simulation characteristics(initialization of simulation).It is found that convolutional autoencoders help to improve the resolution of the seasonal data and successfully reduce the error of the NWP data for 6-months ahead forecasting.Interestingly,the month evidence yields the best agreement indicating a seasonal dependence of the performance.
文摘The aim of this article is to assist farmers in making better crop selection decisions based on soil fertility and weather forecast through the use of IoT and AI (smart farming). To accomplish this, a prototype was developed capable of predicting the best suitable crop for a specific plot of land based on soil fertility and making recommendations based on weather forecast. Random Forest machine learning algorithm was used and trained with Jupyter in the Anaconda framework to achieve an accuracy of about 99%. Based on this process, IoT with the Message Queuing Telemetry Transport (MQTT) protocol, a machine learning algorithm, based on Random Forest, and weather forecast API for crop prediction and recommendations were used. The prototype accepts nitrogen, phosphorus, potassium, humidity, temperature and pH as input parameters from the IoT sensors, as well as the weather API for data forecasting. The approach was tested in a suburban area of Yaounde (Cameroon). Taking into account future meteorological parameters (rainfall, wind and temperature) in this project produced better recommendations and therefore better crop selection. All necessary results can be accessed from anywhere and at any time using the IoT system via a web browser.
