An exploratory spatial data analysis method (ESDA) was designed Apr.28,2002 for kriging monthly rainfall. Samples were monthly rainfall observed at 61 weather stations in eastern China over the period 1961-1998. Comp...An exploratory spatial data analysis method (ESDA) was designed Apr.28,2002 for kriging monthly rainfall. Samples were monthly rainfall observed at 61 weather stations in eastern China over the period 1961-1998. Comparison of five semivariogram models (Spherical, Exponential, Linear, Gaussian and Rational Quadratic) indicated that kriging fulfills the objective of finding better ways to estimate interpolation weights and can provide error information for monthly rainfall interpolation. ESDA yielded the three most common forms of experimental semivariogram for monthly rainfall in the area. All five models were appropriate for monthly rainfall interpolation but under different circumstances. Spherical, Exponential and Linear models perform as smoothing interpolator of the data, whereas Gaussian and Rational Quadratic models serve as an exact interpolator. Spherical, Exponential and Linear models tend to underestimate the values. On the contrary, Gaussian and Rational Quadratic models tend to overestimate the values. Since the suitable model for a specific month usually is not unique and each model does not show any bias toward one or more specific months, an ESDA is recommended for a better interpolation result.展开更多
As a powerful tool to diagnose vertical motion, frontogenesis, and secondary circulation, the Q vector and its divergence are widely used. However, little attention has been given to the curl of Q vector. In this pape...As a powerful tool to diagnose vertical motion, frontogenesis, and secondary circulation, the Q vector and its divergence are widely used. However, little attention has been given to the curl of Q vector. In this paper, a new set of analyses combining the divergence of the Q vector （DQ） with the vertical component of the curl of the Q vector （VQ） is applied to a Northeastern cold vortex rainfall case. From the derivation, it was found that the expressions of the Q vectors and their divergences in saturated moist flow （DQm） differ from those of dry and unsaturated moist atmosphere （DQ）, while the VQs of various background flows are exactly the same, which largely simplified the analyses. This case study showed that, compared with the DQ, not only can the DQm diagnose precipitation more effectively, but the VQ may also be indicative of precipitation （especially for heavy rainfall and strong convection） because of its direct, close relationship with ageostrophic motion. Thus, the VQ may be computed and analyzed with ease, and may serve as a useful tool for analyses of precipitation and strong convective svstems.展开更多
This paper proposes a WD-GA-LSSVM model for predicting the displacement of a deepseated landslide triggered by seasonal rainfall,in which wavelet denoising(WD)is used in displacement time series of landslide to elimin...This paper proposes a WD-GA-LSSVM model for predicting the displacement of a deepseated landslide triggered by seasonal rainfall,in which wavelet denoising(WD)is used in displacement time series of landslide to eliminate the GPS observation noise in the original data,and genetic algorithm(GA)is applied to obtain optimal parameters of least squares support vector machines(LSSVM)model.The model is first trained and then evaluated by using data from a gentle dipping(~2°-5°)landslide triggered by seasonal rainfall in the southwest of China.Performance comparisons of WD-GA-LSSVM model with Back Propagation Neural Network(BPNN)model and LSSVM are presented,individually.The results indicate that the adoption of WD-GA-LSSVM model significantly improves the robustness and accuracy of the displacement prediction and it provides a powerful technique for predicting the displacement of a rainfall-triggered landslide.展开更多
In this paper, the numerical simulation bias of the non-hydrostatic version GRAPES-Meso （Mesoscalc of the Global and Regional Assimilation and Prediction System） at the resolution of 0.18° for a torrential rain...In this paper, the numerical simulation bias of the non-hydrostatic version GRAPES-Meso （Mesoscalc of the Global and Regional Assimilation and Prediction System） at the resolution of 0.18° for a torrential rain case, which happened in May 31st to June 1st 2005 over Hunan province, are diagnosed and investigated by using the radiosondes, intensive surface observation, and the operational global analysis data, and the sensitivity experimental results as well. It is shown in the result that the GRAPES-Meso could reproduce quite well the main features of large-scale circulation and the distribution of the accumulated 24h precipitation and the key locations of tile torrential rainfall arc captured reasonably well by the model. I fowever, bias exist in the simulation of the mesoscale features of the torrential rain and details of the relevant systems. for example, the simulated rainfall that is too earlier in model integration and remsrkable. underpredictien of the peak value of rainfall rates over the heaviest rainfall region, the weakness of the upper jet siimulation and the overpredietion of the south-west wind in the lower troposphere etc. The investigation reveals that the sources of the simulation bias are different. The erroneous model rainfall in the earlier integration stage over the heaviest rainfall region is induced by the model initial condition bias of the wind field at ablaut 925hPa over the torrential rainfall region, where the bias grow rapidly and spread upward to about 600hPa level within the few hours into the integration and result in abnormal convergence of the wind and moisture, and thus the unreal rainfall over that region. The large bias on the simulated rainfall intensity over the heaviest rainfall region might be imputed to the following combined facters of（1） the simulation bias on the strength and detailed structures of the upper-level jet core which bring about significant, underpredictions of the dynamic conditions （including upper-level divergence and the up,yard motion for heavy rainfalt due to unfavorable mesoscale vertical coupting between the strong, upper-level divergence and Iower-level convergence; and （2） the inefficient coupling of the cumulous parameterzation scheme and the explicit moisture in the integration, which causes the failure of the explicit moisture scheme in generating grid-scale rainfall in a certain extent through inadequate convective adjustmenl and feedback to the grid-scale, In addition, the interaction of the combined two factors could form a negative feedback to the rainfall intensity simulation, and eventually lead to the obvious undcrprediction of the rainfall rate.展开更多
Rainfall erosivity in Tibet from 2000 to 2OlO was estimated based on simplified erosion prediction model using daily rainfall data derived from the Tropical Rainfall Measurement Misssion （TRMM） 3B42 rainfall measure...Rainfall erosivity in Tibet from 2000 to 2OlO was estimated based on simplified erosion prediction model using daily rainfall data derived from the Tropical Rainfall Measurement Misssion （TRMM） 3B42 rainfall measurement algorithm. Semi- monthly erosive rainfall and rainfall erosivity were validated using weather station data. The spatial distribution of annual rainfall erosivity as well as its seasonal and annual variation in Tibet was also examined. Results showed that TRMM 3B42 data could serve as an alternative data source to estimate rainfall erosivity in the area where only data from sparsely distributed weather stations are available. The spatial distribution of rainfall erosivity in Tibet generally resembles the distribution of multi-year average of annual rainfall. Annual rainfall erosivity in Tibet decreased from the southeast to the northwest. The concentration degree of rainfall erosivity shows an increasing trend from the southeast to the northwest. High rainfall erosivity accompanies low rainfall erosivity concentration degree and vice versa. Rainfall erosivity increased in the middle and western Tibet and decreased in the southeastern Tibet during the 11 years of this study.展开更多
文摘An exploratory spatial data analysis method (ESDA) was designed Apr.28,2002 for kriging monthly rainfall. Samples were monthly rainfall observed at 61 weather stations in eastern China over the period 1961-1998. Comparison of five semivariogram models (Spherical, Exponential, Linear, Gaussian and Rational Quadratic) indicated that kriging fulfills the objective of finding better ways to estimate interpolation weights and can provide error information for monthly rainfall interpolation. ESDA yielded the three most common forms of experimental semivariogram for monthly rainfall in the area. All five models were appropriate for monthly rainfall interpolation but under different circumstances. Spherical, Exponential and Linear models perform as smoothing interpolator of the data, whereas Gaussian and Rational Quadratic models serve as an exact interpolator. Spherical, Exponential and Linear models tend to underestimate the values. On the contrary, Gaussian and Rational Quadratic models tend to overestimate the values. Since the suitable model for a specific month usually is not unique and each model does not show any bias toward one or more specific months, an ESDA is recommended for a better interpolation result.
基金supported by the National Natural Science Foundation of China under the Grants Nos. 40633016 and 40433007
文摘As a powerful tool to diagnose vertical motion, frontogenesis, and secondary circulation, the Q vector and its divergence are widely used. However, little attention has been given to the curl of Q vector. In this paper, a new set of analyses combining the divergence of the Q vector （DQ） with the vertical component of the curl of the Q vector （VQ） is applied to a Northeastern cold vortex rainfall case. From the derivation, it was found that the expressions of the Q vectors and their divergences in saturated moist flow （DQm） differ from those of dry and unsaturated moist atmosphere （DQ）, while the VQs of various background flows are exactly the same, which largely simplified the analyses. This case study showed that, compared with the DQ, not only can the DQm diagnose precipitation more effectively, but the VQ may also be indicative of precipitation （especially for heavy rainfall and strong convection） because of its direct, close relationship with ageostrophic motion. Thus, the VQ may be computed and analyzed with ease, and may serve as a useful tool for analyses of precipitation and strong convective svstems.
基金supported by the Chinese National Natural Science Foundation (Grant No. 41502293)the National Basic Research Program (973 Program) (Grant No. 2014CB744703)the Funds for Creative Research Groups of China (Grant No. 41521002)
文摘This paper proposes a WD-GA-LSSVM model for predicting the displacement of a deepseated landslide triggered by seasonal rainfall,in which wavelet denoising(WD)is used in displacement time series of landslide to eliminate the GPS observation noise in the original data,and genetic algorithm(GA)is applied to obtain optimal parameters of least squares support vector machines(LSSVM)model.The model is first trained and then evaluated by using data from a gentle dipping(~2°-5°)landslide triggered by seasonal rainfall in the southwest of China.Performance comparisons of WD-GA-LSSVM model with Back Propagation Neural Network(BPNN)model and LSSVM are presented,individually.The results indicate that the adoption of WD-GA-LSSVM model significantly improves the robustness and accuracy of the displacement prediction and it provides a powerful technique for predicting the displacement of a rainfall-triggered landslide.
基金Research into the Theories and Methods for the Monitoring and Prediction of Flood-InflictingTorrential Rains in Southern China - one of Project "973"Study on the Development of Numerical PredictionModels for High-Resolution, Non-Hydrostatic Mesoscale Torrential Rains and Their Prediction Systems
文摘In this paper, the numerical simulation bias of the non-hydrostatic version GRAPES-Meso （Mesoscalc of the Global and Regional Assimilation and Prediction System） at the resolution of 0.18° for a torrential rain case, which happened in May 31st to June 1st 2005 over Hunan province, are diagnosed and investigated by using the radiosondes, intensive surface observation, and the operational global analysis data, and the sensitivity experimental results as well. It is shown in the result that the GRAPES-Meso could reproduce quite well the main features of large-scale circulation and the distribution of the accumulated 24h precipitation and the key locations of tile torrential rainfall arc captured reasonably well by the model. I fowever, bias exist in the simulation of the mesoscale features of the torrential rain and details of the relevant systems. for example, the simulated rainfall that is too earlier in model integration and remsrkable. underpredictien of the peak value of rainfall rates over the heaviest rainfall region, the weakness of the upper jet siimulation and the overpredietion of the south-west wind in the lower troposphere etc. The investigation reveals that the sources of the simulation bias are different. The erroneous model rainfall in the earlier integration stage over the heaviest rainfall region is induced by the model initial condition bias of the wind field at ablaut 925hPa over the torrential rainfall region, where the bias grow rapidly and spread upward to about 600hPa level within the few hours into the integration and result in abnormal convergence of the wind and moisture, and thus the unreal rainfall over that region. The large bias on the simulated rainfall intensity over the heaviest rainfall region might be imputed to the following combined facters of（1） the simulation bias on the strength and detailed structures of the upper-level jet core which bring about significant, underpredictions of the dynamic conditions （including upper-level divergence and the up,yard motion for heavy rainfalt due to unfavorable mesoscale vertical coupting between the strong, upper-level divergence and Iower-level convergence; and （2） the inefficient coupling of the cumulous parameterzation scheme and the explicit moisture in the integration, which causes the failure of the explicit moisture scheme in generating grid-scale rainfall in a certain extent through inadequate convective adjustmenl and feedback to the grid-scale, In addition, the interaction of the combined two factors could form a negative feedback to the rainfall intensity simulation, and eventually lead to the obvious undcrprediction of the rainfall rate.
基金supported by the Natural Science Foundation of China (Grant No. 40925002)the National Science and Technology Supporting Program in the Eleventh Five-Year Plan of China (Grant No. 2007BAC06B06)
文摘Rainfall erosivity in Tibet from 2000 to 2OlO was estimated based on simplified erosion prediction model using daily rainfall data derived from the Tropical Rainfall Measurement Misssion （TRMM） 3B42 rainfall measurement algorithm. Semi- monthly erosive rainfall and rainfall erosivity were validated using weather station data. The spatial distribution of annual rainfall erosivity as well as its seasonal and annual variation in Tibet was also examined. Results showed that TRMM 3B42 data could serve as an alternative data source to estimate rainfall erosivity in the area where only data from sparsely distributed weather stations are available. The spatial distribution of rainfall erosivity in Tibet generally resembles the distribution of multi-year average of annual rainfall. Annual rainfall erosivity in Tibet decreased from the southeast to the northwest. The concentration degree of rainfall erosivity shows an increasing trend from the southeast to the northwest. High rainfall erosivity accompanies low rainfall erosivity concentration degree and vice versa. Rainfall erosivity increased in the middle and western Tibet and decreased in the southeastern Tibet during the 11 years of this study.
