共查询到20条相似文献，搜索用时 657 毫秒
1.
Statistical bias correction methods for numerical weather prediction (NWP) forecasts of maximum and minimum temperatures over India in the medium-range time scale (up to 5 days) are proposed in this study. The objective of bias correction is to minimize the systematic error of the next forecast using bias from past errors. The need for bias corrections arises from the many sources of systematic errors in NWP modeling systems. NWP models have shortcomings in the physical parameterization of weather events and have the inability to handle sub-grid phenomena successfully. The statistical algorithms used for minimizing the bias of the next forecast are running-mean (RM) bias correction, best easy systematic estimator, simple linear regression and the nearest neighborhood (NN) weighted mean, as they are suitable for small samples. Bias correction is done for four global NWP model maximum and minimum temperature forecasts. The magnitude of the bias at a grid point depends upon geographical location and season. Validation of the bias correction methodology is carried out using daily observed and bias-corrected model maximum and minimum temperature forecast over India during July–September 2011. The bias-corrected NWP model forecast generally outperforms direct model output (DMO). The spatial distribution of mean absolute error and root-mean squared error for bias-corrected forecast over India indicate that both the RM and NN methods produce the best skill among other bias correction methods. The inter-comparison reveals that statistical bias correction methods improve the DMO forecast in terms of accuracy in forecast and have the potential for operational applications. 相似文献
2.
An attempt is made in this study to develop a model to forecast the cyclonic depressions leading to cyclonic storms over North Indian Ocean (NIO) with 3 days lead time. A multilayer perceptron (MLP) model is developed for the purpose and the forecast quality of the model is compared with other neural network and multiple linear regression models to assess the forecast skill and performances of the MLP model. The input matrix of the model is prepared with the data of cloud coverage, cloud top temperature, cloud top pressure, cloud optical depth, cloud water path collected from remotely sensed moderate resolution imaging spectro-radiometer (MODIS), and sea surface temperature. The input data are collected 3 days before the cyclogenesis over NIO. The target output is the central pressure, pressure drop, wind speed, and sea surface temperature associated with cyclogenesis over NIO. The models are trained with the data and records from 1998 to 2008. The result of the study reveals that the forecast error with MLP model varies between 0 and 7.2 % for target outputs. The errors with MLP are less than radial basis function network, generalized regression neural network, linear neural network where the errors vary between 0 and 8.4 %, 0.3 and 24.8 %, and 0.3 and 32.4 %, respectively. The forecast with conventional statistical multiple linear regression model, on the other hand, generates error values between 15.9 and 32.4 %. The performances of the models are validated for the cyclonic storms of 2009, 2010, and 2011. The forecast errors with MLP model during validation are also observed to be minimum. 相似文献
3.
Frequent fires can affect ecosystems and public safety. The occurrence of fires has varied with hot and cold months in China. To analyze how temperature influences fire frequency, a fire dataset including 20,622 fires and a historical weather dataset for Changsha in China were gathered and processed. Through data mining, it was found that the mean daily fire frequency tended to be the lowest in the temperature range of (20 °C, 25 °C] and should be related to the low utilization rate of electricity. Through polynomial fitting, it was found that the prediction performance using the daily minimum temperature was generally better than that using the daily maximum temperature, and a quadruplicate polynomial model based on the mean daily minimum temperature of 3 days (the day and the prior 2 days) had the best performance. Then, a temperature-based fire frequency prediction model was established using quadruplicate polynomial regression. Moreover, the results are contrary to the content stipulated in China’s national standard of urban fire-danger weather ratings GB/T 20487-2006. The findings of this study can be applied as technical guidance for fire risk prediction and the revision of GB/T 20487-2006.
相似文献4.
Sutapa Chaudhuri Debashree Dutta Sayantika Goswami Anirban Middey 《Natural Hazards》2013,65(1):97-113
The coastal regions of India are profoundly affected by tropical cyclones during both pre- and post-monsoon seasons with enormous loss of life and property leading to natural disasters. The endeavour of the present study is to forecast the intensity of the tropical cyclones that prevail over Arabian Sea and Bay of Bengal of North Indian Ocean (NIO). A multilayer perceptron (MLP) model is developed for the purpose and compared the forecast through MLP model with other neural network and statistical models to assess the forecast skill and performances of MLP model. The central pressure, maximum sustained surface wind speed, pressure drop, total ozone column and sea surface temperature are taken to form the input matrix of the models. The target output is the intensity of the tropical cyclones as per the T??number. The result of the study reveals that the forecast error with MLP model is minimum (4.70?%) whereas the forecast error with radial basis function network (RBFN) is observed to be 14.62?%. The prediction with statistical multiple linear regression and ordinary linear regression are observed to be 9.15 and 9.8?%, respectively. The models provide the forecast beyond 72?h taking care of the change in intensity at every 3-h interval. The performance of MLP model is tested for severe and very severe cyclonic storms like Mala (2006), Sidr (2007), Nargis (2008), Aila (2009), Laila (2010) and Phet (2010). The forecast errors with MLP model for the said cyclones are also observed to be considerably less. Thus, MLP model in forecasting the intensity of tropical cyclones over NIOs may thus be considered to be an alternative of the conventional operational forecast models. 相似文献
5.
J C JOSHI TANKESHWAR KUMAR SUNITA SRIVASTAVA DIVYA SACHDEVA 《Journal of Earth System Science》2017,126(1):3
Maximum and minimum temperatures are used in avalanche forecasting models for snow avalanche hazard mitigation over Himalaya. The present work is a part of development of Hidden Markov Model (HMM) based avalanche forecasting system for Pir-Panjal and Great Himalayan mountain ranges of the Himalaya. In this work, HMMs have been developed for forecasting of maximum and minimum temperatures for Kanzalwan in Pir-Panjal range and Drass in Great Himalayan range with a lead time of two days. The HMMs have been developed using meteorological variables collected from these stations during the past 20 winters from 1992 to 2012. The meteorological variables have been used to define observations and states of the models and to compute model parameters (initial state, state transition and observation probabilities). The model parameters have been used in the Forward and the Viterbi algorithms to generate temperature forecasts. To improve the model forecasts, the model parameters have been optimised using Baum–Welch algorithm. The models have been compared with persistence forecast by root mean square errors (RMSE) analysis using independent data of two winters (2012–13, 2013–14). The HMM for maximum temperature has shown a 4–12% and 17–19% improvement in the forecast over persistence forecast, for day-1 and day-2, respectively. For minimum temperature, it has shown 6–38% and 5–12% improvement for day-1 and day-2, respectively. 相似文献
6.
Extreme-temperature events have a great impact on human society. Thus, knowledge of summer temperatures can be very useful both for the general public and for organizations whose workers operate in the open. An accurate forecasting of summer maximum and minimum temperatures could help to predict heatwave conditions and permit the implementation of strategies aimed at minimizing the negative effects that high temperatures have on human health. The objective of this work is to evaluate the skill of the regional atmospheric and modelling system (RAMS) model in determining daily summer maximum and minimum temperatures in the Valencia Region. For this, we have used the real-time configuration of this model currently running at the Centro de Estudios Ambientales de Mediterráneo Foundation. This operational system is run twice a day, and both runs have a 3-day forecast range. To carry out the verification of the model in this work, the information generated by the system has been broken into individual simulation days for a specific daily run of the model. Moreover, we have analysed the summer forecast period from 1 June to 31 August for 2007, 2008, 2009 and 2010. The results indicate good agreement between observed and simulated maximum temperatures, with RMSE in general near 2 °C both for coastal and inland stations. For this parameter, the model shows a negative bias around ?1.5 °C in the coast, while the opposite trend is observed inland. In addition, RAMS also shows good results in forecasting minimum temperatures for coastal locations, with bias lower than 1 °C and RMSE below 2 °C. However, the model presents some difficulties for this parameter inland, where bias higher than 3 °C and RMSE of about 4 °C have been found. Besides, there is little difference in both temperatures forecasted within the two daily RAMS cycles and that RAMS is very stable in maintaining the forecast performance at least for three forecast days. 相似文献
7.
基于TIGGE数据的五个单中心集合预报结果(CMA、CMC、ECMWF、NCEP、UKMO)构成的多中心超级集合预报系统的降水量预报,以及相应时段的实测降水量值,应用贝叶斯模式平均法(Bayesian Model Averaging,BMA)建立大渡河流域的BMA概率预报模型。通过CRPS、MAE、BS三种评价指标,对大渡河流域的BMA降水概率预报模型进行评价与检验,三种指标均显示BMA降水概率预报比原始集合预报具有更高的准确性,其中BMA模型的CRPS和MAE指标均值分别相比原始集合预报减少了31.6%和23.9%;分析模型权重参数,得出ECMWF对大渡河流域BMA降水预报贡献最大,即ECMWF对研究区域降水预报效果最好;模型对大渡河流域极端降水预报效果较差,常低估极端降水量。 相似文献
8.
《Comptes Rendus Geoscience》2014,346(9-10):213-222
Two and a half decade (1985–2009) surface air temperature from Giovanni database available for the Naradu valley at High Himalaya Mountain range of Himachal Pradesh has been analysed to determine the changes in the maximum, minimum and mean air temperatures. The analysis was subjected for seasonal, annual and monthly basis and revealed a tendency towards warmer years all around, with significantly warmer winter and more significant increase in minimum temperatures. The annual maximum, minimum and mean temperatures have increase by 1,41 °C, 1,63 °C and 1,49 °C, respectively. The seasonal analysis indicates that the tendency is more pronounced in winter followed by post-monsoon, pre-monsoon and monsoon season. The trends were also examined on a maximum temperatures, and it showed a significant warning in all the months in annual mean, minimum and maximum temperatures, except February during the period of 1985–2009 in the valley. Different trend detection statistical tools have been exercised by using variety of non-parametric tests and all are in agreement. 相似文献
9.
准确可靠的径流预测对于水资源的科学管理与规划具有重要意义,特别是在水资源紧缺的干旱半干旱地区,径流预测对流域内水资源高效利用与水利工程经济运行具有重要现实意义。针对径流预测通常采用单一方法进行建模与预测,难以利用各预测模型优势的问题,本文利用极限学习机（ELM）模型、支持向量机（SVM）模型、多元自适应回归样条（MARS）等机器学习方法建立了疏勒河上游未来1~7日的径流预测模型。在此基础上,运用贝叶斯模型平均（BMA）方法对ELM、SVM、MARS模型的预测结果进行组合,构建了径流组合预测模型,以获取更可靠的预测结果,并采用蒙特卡洛抽样方法获取BMA的95%置信区间,对预测结果进行了不确定性分析。结果表明：ELM、SVM、MARS模型以及BMA组合模型均适用于干旱半干旱地区的中长期日径流预测;BMA的预测精度较单一模型更高,能够提供更准确的预测值;BMA的95%置信区间对实测值覆盖率高,同时能够提供较好的确定性预测和概率预测结果。表明BMA在资料有限的条件下,表现出较单一模型更高的预测性能,可以成为干旱半干旱地区中长期日径流预测的有效方法。 相似文献
10.
介绍了建立水库水情长期数值预报的思路和做法.本法的特点是在逐步回归方法的基础上,同时对所选取的因子进行优化筛选,因而使预报的结果与实测值最为接近.该法用于我国东北地区松花江上游小山、两江和松山等水库的年、月来水量长期预报,结果表明,其预报合格率可达80%以上.另外,对6～9月总流量的预报,根据我国东北地区气候的特点,这一阶段的降水量变异很大,用本法预报会使误差较大,但是年平均流量与这一时段的总流量相关较好,于是提出了另外的预报方法.总之这一方法是较为快捷而实用的水库水情长期预报方法. 相似文献
11.
Ray Kenny 《Geochemical transactions》2018,19(1):2
The upper carbonate member of the Kaibab Formation in northern Arizona (USA) was subaerially exposed during the end Permian and contains fractured and zoned chert rubble lag deposits typical of karst topography. The karst chert rubble has secondary (authigenic) silica precipitates suitable for estimating continental weathering temperatures during the end Permian karst event. New oxygen and hydrogen isotope ratios of secondary silica precipitates in the residual rubble breccia: (1) yield continental palaeotemperature estimates between 17 and 22 °C; and, (2) indicate that meteoric water played a role in the crystallization history of the secondary silica. The continental palaeotemperatures presented herein are broadly consistent with a global mean temperature estimate of 18.2 °C for the latest Permian derived from published climate system models. Few data sets are presently available that allow even approximate quantitative estimates of regional continental palaeotemperatures. These data provide a basis for better understanding the end Permian palaeoclimate at a seasonally-tropical latitude along the western shoreline of Pangaea. 相似文献
12.
Link between convection and meridional gradient of sea surface temperature in the Bay of Bengal 总被引：2，自引：0，他引：2
We use daily satellite estimates of sea surface temperature (SST) and rainfall during 1998–2005 to show that onset of convection
over the central Bay of Bengal (88–92°E, 14–18°N) during the core summer monsoon (mid-May to September) is linked to the meridional
gradient of SST in the bay. The SST gradient was computed between two boxes in the northern (88–92°E, 18–22°N) and southern
(82–88°E, 4–8°N) bay; the latter is the area of the cold tongue in the bay linked to the Summer Monsoon Current. Convection
over central bay followed the SST difference between the northern and southern bay (ΔT) exceeding 0.75°C in 28 cases. There was no instance of ΔT exceeding this threshold without a burst in convection. There were, however, five instances of convection occurring without
this SST gradient. Long rainfall events (events lasting more than a week) were associated with an SST event (ΔT ≥ 0.75°C); rainfall events tended to be short when not associated with an SST event. The SST gradient was important for the
onset of convection, but not for its persistence: convection often persisted for several days even after the SST gradient
weakened. The lag between ΔT exceeding 0.75°C and the onset of convection was 0–18 days, but the lag histogram peaked at one week. In 75% of the 28 cases,
convection occurred within a week of ΔT exceeding the threshold of 0.75°C. The northern bay SST, T
N
, contributed more to ΔT, but it was a weaker criterion for convection than the SST gradient. A sensitivity analysis showed that the corresponding
threshold for T
N
was 29°C. We hypothesise that the excess heating (∼1°C above the threshold for deep convection) required in the northern
bay to trigger convection is because this excess in SST is what is required to establish the critical SST gradient. 相似文献
13.
Computer simulations allow the prediction of hydrocarbon volumes, composition and charge timing in undrilled petroleum prospects. Whereas different models may give different hydrocarbon charge predictions, it has now become evident that a dominant cause of erroneous predictions is the poor quality of input data. The main culprit for prediction errors is the uncertainty in the initial hydrogen index (H/C) of the source rock. A 10% uncertainty in the H/C may lead to 50% error in the predicted hydrocarbon volumes, and associated gas–oil ratio. Similarly, uncertainties in the maximum burial temperature and the kinetics of hydrocarbon generation may lead to 20–50% error. Despite this, charge modelling can have great value for the ranking of prospects in the same area with comparable geological histories. 相似文献
14.
Pijush Samui 《国际地质力学数值与分析法杂志》2012,36(11):1434-1439
The determination of ultimate capacity (Q) of driven piles in cohesionless soil is an important task in geotechnical engineering. This article adopts Multivariate Adaptive Regression Spline (MARS) for prediction Q of driven piles in cohesionless soil. MARS uses length (L), angle of shear resistance of the soil around the shaft (?shaft), angle of shear resistance of the soil at the tip of the pile (?tip), area (A), and effective vertical stress at the tip of the pile as input variables. Q is the output of MARS. The results of MARS are compared with that of the Generalized Regression Neural Network model. An equation has been also presented based on the developed MARS. The results show the strong potential of MARS to be applied to geotechnical engineering as a regression tool. Copyright © 2011 John Wiley & Sons, Ltd. 相似文献
15.
Paula Etala 《Natural Hazards》2009,51(1):49-61
The ability of the SMARA storm surge numerical prediction system to reproduce local effects in estuarine and coastal winds was recently improved by considering one-way coupling of the air–sea momentum exchange through the wave stress, and best forecasting practices for downscaling. The inclusion of long period atmospheric pressure forcing in tide and tide/surge calculations corrected a systematic error in the surge, produced by the South Atlantic Ocean quasi-stationary pressure patterns. The maximum forecast range for the storm surge at Buenos Aires provided by the real-time use of water level observations is approximately 12 h. The best available water level prediction is the 6-h forecast (nowcast) based on the closest water level observations. The 24-h forecast from the numerical models slightly improves this nowcast. Although the numerical forecast accuracy degrades after the first 48 h, the improvement to the full range observation-based prediction is maintained at the inner Río de la Plata area and extends to the first 3 days at the intermediate navigation channels. 相似文献
16.
In this paper, we have utilized ANN (artificial neural network) modeling for the prediction of monthly rainfall in Mashhad synoptic station which is located in Iran. To achieve this black-box model, we have used monthly rainfall data from 1953 to 2003 for this synoptic station. First, the Hurst rescaled range statistical (R/S) analysis is used to evaluate the predictability of the collected data. Then, to extract the rainfall dynamic of this station using ANN modeling, a three-layer feed-forward perceptron network with back propagation algorithm is utilized. Using this ANN structure as a black-box model, we have realized the complex dynamics of rainfall through the past information of the system. The approach employs the gradient decent algorithm to train the network. Trying different parameters, two structures, M531 and M741, have been selected which give the best estimation performance. The performance statistical analysis of the obtained models shows with the best tuning of the developed monthly prediction model the correlation coefficient (R), root mean square error (RMSE), and mean absolute error (MAE) are 0.93, 0.99, and 6.02 mm, respectively, which confirms the effectiveness of the developed models. 相似文献
17.
In this paper, the performance of a high-resolution mesoscale model for the prediction of severe tropical cyclones over the Bay of Bengal during 2007?C2010 (Sidr, Nargis, Aila, and Laila) is discussed. The advanced Weather Research Forecast (WRF) modeling system (ARW core) is used with a combination of Yonsei University PBL schemes, Kain-Fritsch cumulus parameterization, and Ferrier cloud microphysics schemes for the simulations. The initial and boundary conditions for the simulations are derived from global operational analysis and forecast products of the National Center for Environmental Prediction-Global Forecast System (NCEP-GFS) available at 1°lon/lat resolution. The simulation results of the extreme weather parameters such as heavy rainfall, strong wind and track of those four severe cyclones, are critically evaluated and discussed by comparing with the Joint Typhoon Warning Center (JTWC) estimated values. The simulations of the cyclones reveal that the cyclone track, intensity, and time of landfall are reasonably well simulated by the model. The mean track error at the time of landfall of the cyclone is 98?km, in which the minimum error was found to be for the cyclone Nargis (22?km) and maximum error for the cyclone Laila (304?km). The landfall time of all the cyclones is also fairly simulated by the model. The distribution and intensity of rainfall are well simulated by the model as well and were comparable with the TRMM estimates. 相似文献
18.
基于核最小二乘模型的矿产靶区预测 总被引：1，自引：0，他引：1
地质统计单元的含矿性与地质找矿证据之间存在复杂的非线性关系,建立这种复杂关系的多元非线性统计模型并预测矿产靶区,对矿产勘查具有重要指导意义。以核函数为理论工具,在核最小二乘原理基础上提出了矿产靶区预测的核最小二乘模型;在GDAL数字图像输入输出函数库和CLAPACK线性代数软件包基础上,用VC++语言开发了面向栅格数据的矿产靶区预测核最小二乘模型算法程序,并把模型应用于新疆阿勒泰地区的矿产靶区预测研究。在MapInfo中生成包含100×151个网格统计单元的栅格图层,把栅格化后的15种找矿证据图层转化成100×151×15的数字图像数据立方体,用自行开发的程序计算每个网格统计单元的核最小二乘判别得分。结果表明,网格统计单元判别得分的高值区与已知矿床（点）的空间分布基本一致。 相似文献
19.
Tomi P. Luoto 《Environmental Earth Sciences》2013,68(5):1321-1329
Climate dynamics during the past ca. 700 years in southern Finland were reconstructed using fossil midge (Diptera:Nematocera) assemblages aiming to estimate quantitatively the temperature change that has occurred from the Little Ice Age to the present. Midge stratigraphies of two sediment cores from eastern and southern Finland were chosen to be examined for temperature inferences utilizing the modern analogue technique. The new midge-based temperature inference model had a coefficient of determination of 0.901 and a prediction error of 0.498 °C, showing improvement over the previous Finnish models. The combined curve of the inferred temperatures derived from both of the cores showed a decrease towards ca. 1700 AD, when temperatures were approximately 1 °C cooler than the past 700 years average and almost 2 °C colder than present. The temperatures began to increase in southern Finland from 1800 AD and in eastern Finland from 1900 AD onwards. The highest temperatures were reached at the top of the core, representing the present climate warming. Although there was slight overestimation in the recent inferred values, the reconstructed trends were in close correspondence with the previous proxy-based, historical, and measured data that suggests that the reconstruction was realistic and reliable. 相似文献
20.
为了预测边坡变形大小,以便及时采取防治措施,通过取不同的迟滞时间对原始监测数据进行相空间重置获得新的数据序列,再以这些新的数据序列为基础,采用预测残差平方和最小的线性优化方法,将ARMA时间序列法和GM（1,1）灰色理论的预测结果进行组合,对某矿的边坡变形量进行滚动组合预测。对不同方法的预测精度和预测残差的标准差进行了对比分析,结果对比表明,组合预测较单项预测方法残差标准差明显减小,且相空间重置后的组合预测结果提高了直接预测的平均预测精度,这为边坡变形趋势的预测与防灾预警提供了可靠的方法。 相似文献