为提高天气研究和预报(Weather Research and Forecasting,WRF)数值模式风速预报的准确度,引入深度置信网络(Deep Belief Nets,DBN),构建了基于WRF数值模型的DBN风速预测模型。利用WRF数值模式进行风速预报,将预报结果与70m高的测风塔...为提高天气研究和预报(Weather Research and Forecasting,WRF)数值模式风速预报的准确度,引入深度置信网络(Deep Belief Nets,DBN),构建了基于WRF数值模型的DBN风速预测模型。利用WRF数值模式进行风速预报,将预报结果与70m高的测风塔实际数据作为网络的输入对深度信念网络进行逐层训练,在Matlab平台上建立DBN风速预测模型并进行仿真。经验证:基于WRF数值模式的DBN风速预测模型的相对均方根误差为11.03%,比支持向量机(Support Vector Machine,SVM)预测模型降低了4.41%。实验结果表明:该模型能很好地预测风速并且得到了较高的预测精度。展开更多
Limitations in the predictability of quantitative precipitation forecasting (QPF) that arise from initial errors of small amplitude and scale are investigated by means of real-case high-resolution (cloud-resolving) nu...Limitations in the predictability of quantitative precipitation forecasting (QPF) that arise from initial errors of small amplitude and scale are investigated by means of real-case high-resolution (cloud-resolving) numerical weather prediction (NWP) integrations. The case considered is the hail and wind disaster that occurred in Sichuan on 8 April 2005. A total of three distinct perturbation methods are used. The results suggest that a tiny initial error in the temperature field can amplify and influence the weather in a large domain, changing the 12-h forecasted rainfall by as much as one-third of the original magnitude. Furthermore, the comparison of the perturbation methods indicates that all of the methods pinpoint the same region (the heavy rainfall areas in the control experiment) as suffering from limitations in predictability. This result reveals the important role of nonlinearity in severe convective events.展开更多
Process of sea surface diurnal warming has drawn a lot of attention in recent years, but that occurs in shelf seas was rarely addressed. In the present work, surface diurnal warming strength in the East China Sea was ...Process of sea surface diurnal warming has drawn a lot of attention in recent years, but that occurs in shelf seas was rarely addressed. In the present work, surface diurnal warming strength in the East China Sea was calculated by the sea surface temperature(SST) data derived from the MODIS sensors carried by the satellites Aqua and Terra. Due to transit time difference, both the number of valid data and the surface diurnal warming strength computed by the MODIS-Aqua data are relatively larger than Terra. Therefore, the 10-year MODIS-Aqua data from 2005 to 2014 were used to analyze the monthly variability of the surface diurnal warming. Generally, the surface diurnal warming in the East China sea is stronger in summer and autumn but weaker in winter and spring, while it shows different peaks in different regions. Large events with ΔT≥5 K have also been discussed. They were found mainly in coastal area, especially near the Changjiang(Yangtze) River estuary. And there exists a high-incidence period from April to July. Furthermore, the relationship between surface diurnal warming and wind speed was discussed. Larger diurnal warming mainly lies in areas with low wind speed. And its possibility decreases with the increase of wind speed. Events with ΔT ≥2.5 K rarely occur when wind speed is over 12 m/s. Study on surface diurnal warming in the East China Sea may help to understand the daily scale air-sea interaction in the shelf seas. A potential application might be in the marine weather forecasts by numerical models. Its impact on the coastal eco-system and the activities of marine organisms can also be pursued.展开更多
Flash floods are a natural disaster that occurs annually, especially in the mountainous terrain and steep slopes of northern Thailand. The current flood forecasting systems and tools are available but have low accurac...Flash floods are a natural disaster that occurs annually, especially in the mountainous terrain and steep slopes of northern Thailand. The current flood forecasting systems and tools are available but have low accuracy and efficiency. The numbers of rainfall and runoff stations are less, because the access to the station area is difficult. Additionally, the operation and maintenance costs are high. Hydrological modeling of a SWAT （Soil and Water Assessment Tool） was used in this study with the application of three days weather forecast from the NWP （numerical weather prediction）, which provided temperature, relative humidity, rainfall, sunshine and wind speed. The data from NWP and SWAT were used to simulate the runoff from the Nan River in the last 10 years （2000-2010）. It was found that the simulated flow rate for the main streams using data from NWP were higher than the observations. At the N64 and Nl stations, the ratios of the maximum simulated flow rate to the observations were equal to 108% and 118%, respectively. However, for the tributaries, it was found that the simulated flow rate using NWP data was lower than the observations, but, it was still within the acceptable range of not greater than 20%,6. At N65, D090201 and D090203 stations, the ratio of the maximum simulated flow rate were 90.0%, 83.0% and 86.0%, respectively. This was due to the rainfall from the NWP model being greater than the measured rainfall. The NWP rainfall was distributed all over the area while the rainfall data from the measurements were obtained from specific points. Therefore, the rain from the NWP model is very useful especially for the watershed areas without rain gauge stations. In summary, the data from the NWP can be used with the SWAT model and provides relatively sound results despite the value for the main river being slightly higher than the observed data. Consequently, the output can be used to create a flood map for flash flood warning in the area.展开更多
Data assimilation is the process by which measurements and model predictions are combined to obtain an accurate representation of the state of the modeled system. We implemented a data assimilation scheme called LETKF...Data assimilation is the process by which measurements and model predictions are combined to obtain an accurate representation of the state of the modeled system. We implemented a data assimilation scheme called LETKF （local ensemble transform Kalman filter） with FSUGSM （Florida State University Global Spectral Model） and made an experiment to evaluate the initial condition generated to numerical weather prediction to FSUGSM model. The LETKF analysis carries out independently at each grid point with the use of ＂local＂ observations. An ensemble of estimates in state space represents uncertainty. The FSUGSM is a multilevel （27 vertical levels） spectral primitive equation model, where the variables are expanded horizontally in a truncated series of spherical harmonic functions （at resolution T63） and a transform technique is applied to calculate the physical processes in real space The assimilation cycle runs on the period 01/01/2001 to 31/01/2001 at （00, 06, 12 and 18 GMT） for each day. We examined the atmospheric fields during the period and the OMF （observation-minus-forecast） and the OMA （observation-minus-analysis） statistics to verify the analysis quality comparing with forecasts and observations. The analyses present stability and show suitable to initiate the weather predictions.展开更多
文摘为提高天气研究和预报(Weather Research and Forecasting,WRF)数值模式风速预报的准确度,引入深度置信网络(Deep Belief Nets,DBN),构建了基于WRF数值模型的DBN风速预测模型。利用WRF数值模式进行风速预报,将预报结果与70m高的测风塔实际数据作为网络的输入对深度信念网络进行逐层训练,在Matlab平台上建立DBN风速预测模型并进行仿真。经验证:基于WRF数值模式的DBN风速预测模型的相对均方根误差为11.03%,比支持向量机(Support Vector Machine,SVM)预测模型降低了4.41%。实验结果表明:该模型能很好地预测风速并且得到了较高的预测精度。
基金supported by the National Natural Science Foundation of China (Grant No. 40775067)
文摘Limitations in the predictability of quantitative precipitation forecasting (QPF) that arise from initial errors of small amplitude and scale are investigated by means of real-case high-resolution (cloud-resolving) numerical weather prediction (NWP) integrations. The case considered is the hail and wind disaster that occurred in Sichuan on 8 April 2005. A total of three distinct perturbation methods are used. The results suggest that a tiny initial error in the temperature field can amplify and influence the weather in a large domain, changing the 12-h forecasted rainfall by as much as one-third of the original magnitude. Furthermore, the comparison of the perturbation methods indicates that all of the methods pinpoint the same region (the heavy rainfall areas in the control experiment) as suffering from limitations in predictability. This result reveals the important role of nonlinearity in severe convective events.
基金Supported by the Zhejiang Provincial Natural Science Foundation(No.LY17D060003)the Shandong Provincial Natural Science Foundation(No.ZR2015DQ006)+1 种基金the National Narutal Science Foundation of China(Nos.41306035,41206006)the National Key R&D Plan of China(No.2016YFC1401404)
文摘Process of sea surface diurnal warming has drawn a lot of attention in recent years, but that occurs in shelf seas was rarely addressed. In the present work, surface diurnal warming strength in the East China Sea was calculated by the sea surface temperature(SST) data derived from the MODIS sensors carried by the satellites Aqua and Terra. Due to transit time difference, both the number of valid data and the surface diurnal warming strength computed by the MODIS-Aqua data are relatively larger than Terra. Therefore, the 10-year MODIS-Aqua data from 2005 to 2014 were used to analyze the monthly variability of the surface diurnal warming. Generally, the surface diurnal warming in the East China sea is stronger in summer and autumn but weaker in winter and spring, while it shows different peaks in different regions. Large events with ΔT≥5 K have also been discussed. They were found mainly in coastal area, especially near the Changjiang(Yangtze) River estuary. And there exists a high-incidence period from April to July. Furthermore, the relationship between surface diurnal warming and wind speed was discussed. Larger diurnal warming mainly lies in areas with low wind speed. And its possibility decreases with the increase of wind speed. Events with ΔT ≥2.5 K rarely occur when wind speed is over 12 m/s. Study on surface diurnal warming in the East China Sea may help to understand the daily scale air-sea interaction in the shelf seas. A potential application might be in the marine weather forecasts by numerical models. Its impact on the coastal eco-system and the activities of marine organisms can also be pursued.
文摘Flash floods are a natural disaster that occurs annually, especially in the mountainous terrain and steep slopes of northern Thailand. The current flood forecasting systems and tools are available but have low accuracy and efficiency. The numbers of rainfall and runoff stations are less, because the access to the station area is difficult. Additionally, the operation and maintenance costs are high. Hydrological modeling of a SWAT （Soil and Water Assessment Tool） was used in this study with the application of three days weather forecast from the NWP （numerical weather prediction）, which provided temperature, relative humidity, rainfall, sunshine and wind speed. The data from NWP and SWAT were used to simulate the runoff from the Nan River in the last 10 years （2000-2010）. It was found that the simulated flow rate for the main streams using data from NWP were higher than the observations. At the N64 and Nl stations, the ratios of the maximum simulated flow rate to the observations were equal to 108% and 118%, respectively. However, for the tributaries, it was found that the simulated flow rate using NWP data was lower than the observations, but, it was still within the acceptable range of not greater than 20%,6. At N65, D090201 and D090203 stations, the ratio of the maximum simulated flow rate were 90.0%, 83.0% and 86.0%, respectively. This was due to the rainfall from the NWP model being greater than the measured rainfall. The NWP rainfall was distributed all over the area while the rainfall data from the measurements were obtained from specific points. Therefore, the rain from the NWP model is very useful especially for the watershed areas without rain gauge stations. In summary, the data from the NWP can be used with the SWAT model and provides relatively sound results despite the value for the main river being slightly higher than the observed data. Consequently, the output can be used to create a flood map for flash flood warning in the area.
文摘Data assimilation is the process by which measurements and model predictions are combined to obtain an accurate representation of the state of the modeled system. We implemented a data assimilation scheme called LETKF （local ensemble transform Kalman filter） with FSUGSM （Florida State University Global Spectral Model） and made an experiment to evaluate the initial condition generated to numerical weather prediction to FSUGSM model. The LETKF analysis carries out independently at each grid point with the use of ＂local＂ observations. An ensemble of estimates in state space represents uncertainty. The FSUGSM is a multilevel （27 vertical levels） spectral primitive equation model, where the variables are expanded horizontally in a truncated series of spherical harmonic functions （at resolution T63） and a transform technique is applied to calculate the physical processes in real space The assimilation cycle runs on the period 01/01/2001 to 31/01/2001 at （00, 06, 12 and 18 GMT） for each day. We examined the atmospheric fields during the period and the OMF （observation-minus-forecast） and the OMA （observation-minus-analysis） statistics to verify the analysis quality comparing with forecasts and observations. The analyses present stability and show suitable to initiate the weather predictions.
