2018 Vol. 24, No. 2
2018, 24(2): 123-130.
doi: 10.16555/j.1006-8775.2018.02.001
Abstract:
The basic structure and cloud features of Typhoon Nida (2016) are simulated using a new microphysics scheme (Liuma) within the Weather Research and Forecasting (WRF) model. Typhoon characteristics simulated with the Liuma microphysics scheme are compared with observations and those simulated with a commonly- used microphysics scheme (WSM6). Results show that using different microphysics schemes does not significantly alter the track of the typhoon but does significantly affect the intensity and the cloud structure of the typhoon. Results also show that the vertical distribution of cloud hydrometeors and the horizontal distribution of peripheral rainband are affected by the microphysics scheme. The mixing ratios of rain water and graupel correlate highly with the vertical velocity component and equivalent potential temperature at the typhoon eye-wall region. According to the simulation with WSM 6 scheme, it is likely that the very low typhoon central pressure results from the positive feedback between hydrometeors and typhoon intensity. As the ice-phase hydrometeors are mostly graupel in the Liuma microphysics scheme, further improvement in this aspect is required.
The basic structure and cloud features of Typhoon Nida (2016) are simulated using a new microphysics scheme (Liuma) within the Weather Research and Forecasting (WRF) model. Typhoon characteristics simulated with the Liuma microphysics scheme are compared with observations and those simulated with a commonly- used microphysics scheme (WSM6). Results show that using different microphysics schemes does not significantly alter the track of the typhoon but does significantly affect the intensity and the cloud structure of the typhoon. Results also show that the vertical distribution of cloud hydrometeors and the horizontal distribution of peripheral rainband are affected by the microphysics scheme. The mixing ratios of rain water and graupel correlate highly with the vertical velocity component and equivalent potential temperature at the typhoon eye-wall region. According to the simulation with WSM 6 scheme, it is likely that the very low typhoon central pressure results from the positive feedback between hydrometeors and typhoon intensity. As the ice-phase hydrometeors are mostly graupel in the Liuma microphysics scheme, further improvement in this aspect is required.
2018, 24(2): 131-141.
doi: 10.16555/j.1006-8775.2018.02.002
Abstract:
Using data of tropical cyclones making landfall in China between May and October each year during the 1951-2015 period from the Shanghai Typhoon Institute, China Meteorological Administration (CMA-STI) Tropical Cyclone (TC) Best Track Dataset, we developed a method of rapid classification of TC tracks based on their average movement velocities and noted three types of tracks: a westward type, a northwestward type, and a northward type. We compared the climate characteristics of the westward and northward types and discuss their corresponding causes. The results show that the westward and northward types account for more than 80% of all TCs making landfall in China. Their climate characteristics, such as the frequency, landfall intensity, duration over land, velocity over land, movement distance over land, and other changes, show both similarities and differences. Both TC types show significant increases in their over-land durations, indicating that the effects of these landfalling TCs are increasing. However, the causes of these two TC types are similar and different in certain respects. The changes in large-scale steering flows have significantly affected the frequencies and over-land velocities of the landfalling TCs of the westward and northward types. In addition, differences between the changes in formation locations of the westward and northward types may lead to significant difference in their landfall intensities.
Using data of tropical cyclones making landfall in China between May and October each year during the 1951-2015 period from the Shanghai Typhoon Institute, China Meteorological Administration (CMA-STI) Tropical Cyclone (TC) Best Track Dataset, we developed a method of rapid classification of TC tracks based on their average movement velocities and noted three types of tracks: a westward type, a northwestward type, and a northward type. We compared the climate characteristics of the westward and northward types and discuss their corresponding causes. The results show that the westward and northward types account for more than 80% of all TCs making landfall in China. Their climate characteristics, such as the frequency, landfall intensity, duration over land, velocity over land, movement distance over land, and other changes, show both similarities and differences. Both TC types show significant increases in their over-land durations, indicating that the effects of these landfalling TCs are increasing. However, the causes of these two TC types are similar and different in certain respects. The changes in large-scale steering flows have significantly affected the frequencies and over-land velocities of the landfalling TCs of the westward and northward types. In addition, differences between the changes in formation locations of the westward and northward types may lead to significant difference in their landfall intensities.
2018, 24(2): 142-150.
doi: 10.16555/j.1006-8775.2018.02.003
Abstract:
The classification of tropical cyclones (TCs) is significant to obtain their temporal and spatial variation characteristics in the context of dramatic-changing global climate. A new TCs clustering method by using K-means clustering algorithm with nine physical indexes is proposed in the paper. Each TC is quantified into an 11-dimensional vector concerning trajectory attributes, time attributes and power attributes. Two recurving clusters (cluster A and E) and three straight-moving clusters (cluster B, C and D) are categorized from the TC best-track dataset of the western North Pacific (WNP) over the period of 1949-2013, and TCs’ properties have been analyzed and compared in different aspects. The calculation results of coefficient variation (CV) and Nash-Sutcliffe efficiency (NSE) reveal a high level of intra-cluster cohesiveness and inter-cluster divergence, which means that the physical index system could serve as a feasible method of TCs classification. The clusters are then analyzed in terms of trajectory, lifespan, seasonality, trend, intensity and Power Dissipation Index (PDI). The five classified clusters show distinct features in TCs’ temporal and spatial development discipline. Moreover, each cluster has its individual motion pattern, variation trend, influence region and impact degree.
The classification of tropical cyclones (TCs) is significant to obtain their temporal and spatial variation characteristics in the context of dramatic-changing global climate. A new TCs clustering method by using K-means clustering algorithm with nine physical indexes is proposed in the paper. Each TC is quantified into an 11-dimensional vector concerning trajectory attributes, time attributes and power attributes. Two recurving clusters (cluster A and E) and three straight-moving clusters (cluster B, C and D) are categorized from the TC best-track dataset of the western North Pacific (WNP) over the period of 1949-2013, and TCs’ properties have been analyzed and compared in different aspects. The calculation results of coefficient variation (CV) and Nash-Sutcliffe efficiency (NSE) reveal a high level of intra-cluster cohesiveness and inter-cluster divergence, which means that the physical index system could serve as a feasible method of TCs classification. The clusters are then analyzed in terms of trajectory, lifespan, seasonality, trend, intensity and Power Dissipation Index (PDI). The five classified clusters show distinct features in TCs’ temporal and spatial development discipline. Moreover, each cluster has its individual motion pattern, variation trend, influence region and impact degree.
2018, 24(2): 151-162.
doi: 10.16555/j.1006-8775.2018.02.004
Abstract:
The Microwave Temperature Sounder-II (MWTS-II) and Microwave Humidity and Temperature Sounder (MWHTS) onboard the Fengyun-3C (FY-3C) satellite can be used to detect atmospheric temperature profiles. The MWTS-II has 13 temperature sounding channels around the 60 GHz oxygen absorption band and the MWHTS has 8 temperature sounding channels around the 118.75 GHz oxygen absorption line. The data quality of the observed brightness temperatures can be evaluated using atmospheric temperature retrievals from the MWTS-II and MWHTS observations. Here, the bias characteristics and corrections of the observed brightness temperatures are described. The information contents of observations are calculated, and the retrieved atmospheric temperature profiles are compared using a neural network (NN) retrieval algorithm and a one-dimensional variational inversion (1D-var) retrieval algorithm. The retrieval results from the NN algorithm show that the accuracy of the MWTS-II retrieval is higher than that of the MWHTS retrieval, which is consistent with the results of the radiometric information analysis. The retrieval results from the 1D-var algorithm show that the accuracy of MWTS-II retrieval is similar to that of the MWHTS retrieval at the levels from 850–C1,000 hPa, is lower than that of the MWHTS retrieval at the levels from 650–C850 hPa and 125–C300 hPa, and is higher than that of MWHTS at the other levels. Comparison of the retrieved atmospheric temperature using these satellite observations provides a reference value for assessing the accuracy of atmospheric temperature detection at the 60 GHz oxygen band and 118.75 GHz oxygen line. In addition, based on the comparison of the retrieval results, an optimized combination method is proposed using a branch and bound algorithm for the NN retrieval algorithm, which combines the observations from both the MWTS-II and MWHTS instruments to retrieve the atmospheric temperature profiles. The results show that the optimal combination can further improve the accuracy of MWTS-II retrieval and enhance the detection accuracy of atmospheric temperatures near the surface.
The Microwave Temperature Sounder-II (MWTS-II) and Microwave Humidity and Temperature Sounder (MWHTS) onboard the Fengyun-3C (FY-3C) satellite can be used to detect atmospheric temperature profiles. The MWTS-II has 13 temperature sounding channels around the 60 GHz oxygen absorption band and the MWHTS has 8 temperature sounding channels around the 118.75 GHz oxygen absorption line. The data quality of the observed brightness temperatures can be evaluated using atmospheric temperature retrievals from the MWTS-II and MWHTS observations. Here, the bias characteristics and corrections of the observed brightness temperatures are described. The information contents of observations are calculated, and the retrieved atmospheric temperature profiles are compared using a neural network (NN) retrieval algorithm and a one-dimensional variational inversion (1D-var) retrieval algorithm. The retrieval results from the NN algorithm show that the accuracy of the MWTS-II retrieval is higher than that of the MWHTS retrieval, which is consistent with the results of the radiometric information analysis. The retrieval results from the 1D-var algorithm show that the accuracy of MWTS-II retrieval is similar to that of the MWHTS retrieval at the levels from 850–C1,000 hPa, is lower than that of the MWHTS retrieval at the levels from 650–C850 hPa and 125–C300 hPa, and is higher than that of MWHTS at the other levels. Comparison of the retrieved atmospheric temperature using these satellite observations provides a reference value for assessing the accuracy of atmospheric temperature detection at the 60 GHz oxygen band and 118.75 GHz oxygen line. In addition, based on the comparison of the retrieval results, an optimized combination method is proposed using a branch and bound algorithm for the NN retrieval algorithm, which combines the observations from both the MWTS-II and MWHTS instruments to retrieve the atmospheric temperature profiles. The results show that the optimal combination can further improve the accuracy of MWTS-II retrieval and enhance the detection accuracy of atmospheric temperatures near the surface.
2018, 24(2): 163-175.
doi: 10.16555/j.1006-8775.2018.02.005
Abstract:
In order to achieve the best predictive effect of the Partial Least Squares (PLS) regression model, Particle Swarm Optimization (PSO) algorithm is applied to automatically filter the optimal subset of a set of candidate factors of PLS regression model in this study. An improved version of the Particle Swarm Optimization-Partial Least Squares (PSO-PLS) regression model is applied to the station data of precipitation in Southwest China during flood season. Using the PSO-PLS regression method, the prediction of flood season precipitation in Southwest China has been studied. By introducing the precipitation period series of the mean generating function (MGF) extension as an alternative factor, the MGF improved PSO-PLS regression model was also build up to improve the prediction results. Randomly selected 10%, 20%, 30% of the modeling samples were used as a test trial; random cross validation was conducted on the MGF improved PSO-PLS regression model. The results show that the accuracy of PSO-PLS regression model and the MGF improved PSO-PLS regression model are better than that of the traditional PLS regression model. The training results of the three prediction models with regard to the regional and single station precipitation are considerable, whereas the forecast results indicate that the PSO-PLS regression method and the MGF improved PSO-PLS regression method are much better than the traditional PLS regression method. The MGF improved PSO-PLS regression model has the best forecast performance on precipitation anomaly during the flood season in the southwest of China among three models. The average precipitation (PS score) of 36 stations is 74.7. With the increase of the number of modeling samples, the PS score remained stable. This shows that the PSO algorithm is objective and stable. The MGF improved PSO-PLS regression prediction model is also showed to have good prediction stability and ability.
In order to achieve the best predictive effect of the Partial Least Squares (PLS) regression model, Particle Swarm Optimization (PSO) algorithm is applied to automatically filter the optimal subset of a set of candidate factors of PLS regression model in this study. An improved version of the Particle Swarm Optimization-Partial Least Squares (PSO-PLS) regression model is applied to the station data of precipitation in Southwest China during flood season. Using the PSO-PLS regression method, the prediction of flood season precipitation in Southwest China has been studied. By introducing the precipitation period series of the mean generating function (MGF) extension as an alternative factor, the MGF improved PSO-PLS regression model was also build up to improve the prediction results. Randomly selected 10%, 20%, 30% of the modeling samples were used as a test trial; random cross validation was conducted on the MGF improved PSO-PLS regression model. The results show that the accuracy of PSO-PLS regression model and the MGF improved PSO-PLS regression model are better than that of the traditional PLS regression model. The training results of the three prediction models with regard to the regional and single station precipitation are considerable, whereas the forecast results indicate that the PSO-PLS regression method and the MGF improved PSO-PLS regression method are much better than the traditional PLS regression method. The MGF improved PSO-PLS regression model has the best forecast performance on precipitation anomaly during the flood season in the southwest of China among three models. The average precipitation (PS score) of 36 stations is 74.7. With the increase of the number of modeling samples, the PS score remained stable. This shows that the PSO algorithm is objective and stable. The MGF improved PSO-PLS regression prediction model is also showed to have good prediction stability and ability.
2018, 24(2): 185-198.
doi: 10.16555/j.1006-8775.2018.02.007
Abstract:
Data from high-resolution satellites were used to evaluate the spatial and temporal distribution of mesoscale convective vortices (MCVs) in central and east China and the western Pacific Ocean region. The monthly variation in MCVs was significant. From May to October, MCVs were clearly affected by large-scale environmental conditions, including the South Asian summer monsoon, subtropical high and solar radiation, which resulted in clear changes in MCV spatial distributions from strengthening and weakening processes. Based on the analysis of diurnal MCV variations and the precipitation rate from May to October, MCVs were found to occur more frequently over the ocean than over land. MCVs near the Sea of Japan and northern South China Sea occurred during all types of weather. Ocean occurrences near land, such as the Ryukyu Islands, were categorized as morning-active MCVs. The hilly regions of southeastern China and North China Plain were characterized by afternoon-active MCVs. Limited to topography and the urban heat island effect, the Beijing-Tianjin-Tangshan area had evening-active MCVs, while Changbai Mountain had nocturnal MCVs.
Data from high-resolution satellites were used to evaluate the spatial and temporal distribution of mesoscale convective vortices (MCVs) in central and east China and the western Pacific Ocean region. The monthly variation in MCVs was significant. From May to October, MCVs were clearly affected by large-scale environmental conditions, including the South Asian summer monsoon, subtropical high and solar radiation, which resulted in clear changes in MCV spatial distributions from strengthening and weakening processes. Based on the analysis of diurnal MCV variations and the precipitation rate from May to October, MCVs were found to occur more frequently over the ocean than over land. MCVs near the Sea of Japan and northern South China Sea occurred during all types of weather. Ocean occurrences near land, such as the Ryukyu Islands, were categorized as morning-active MCVs. The hilly regions of southeastern China and North China Plain were characterized by afternoon-active MCVs. Limited to topography and the urban heat island effect, the Beijing-Tianjin-Tangshan area had evening-active MCVs, while Changbai Mountain had nocturnal MCVs.
2018, 24(2): 199-208.
doi: 10.16555/j.1006-8775.2018.02.008
Abstract:
Based on meteorological data including daily sunshine duration, temperature and precipitation from 97 meteorological stations in Hunan province during the period of 1981-2010, in combination with the field experiment in different places at different sowing dates, the precise climatic risk zoning of double cropping super rice cultivation has been studied by using the spatial interpolation method and other Geographical Information System (GIS) technologies. Three key climatic factors were selected including chilling in May, high temperature heat damage during July to early August and low temperature damage in autumn in this study. Furthermore, based on the analysis of climatic conditions suitable for double cropping super rice cultivation and climatic disasters, 8~22?C active accumulated temperature, sunshine duration from late March to October, climatic risk index of the low temperature in autumn, and climatic risk index of chilling in May were selected as key climatic factors to study the precise agro-meteorological regionalization of double cropping super rice in Hunan province. The results showed that: the high-yielding zones of double cropping super rice in Hunan were mainly located in Zhuzhou, Hengyang, Yongzhou and Chenzhou City, the moderate-yielding zones were primarily located in the east and north reaches of Dongting Lake, together with most of Changsha, Zhuzhou and Xiangtan City, and other regions in Hunan were not suitable for double cropping super rice. These findings can provide valuable information for the large-scale cultivation of double cropping super rice in Hunan province.
Based on meteorological data including daily sunshine duration, temperature and precipitation from 97 meteorological stations in Hunan province during the period of 1981-2010, in combination with the field experiment in different places at different sowing dates, the precise climatic risk zoning of double cropping super rice cultivation has been studied by using the spatial interpolation method and other Geographical Information System (GIS) technologies. Three key climatic factors were selected including chilling in May, high temperature heat damage during July to early August and low temperature damage in autumn in this study. Furthermore, based on the analysis of climatic conditions suitable for double cropping super rice cultivation and climatic disasters, 8~22?C active accumulated temperature, sunshine duration from late March to October, climatic risk index of the low temperature in autumn, and climatic risk index of chilling in May were selected as key climatic factors to study the precise agro-meteorological regionalization of double cropping super rice in Hunan province. The results showed that: the high-yielding zones of double cropping super rice in Hunan were mainly located in Zhuzhou, Hengyang, Yongzhou and Chenzhou City, the moderate-yielding zones were primarily located in the east and north reaches of Dongting Lake, together with most of Changsha, Zhuzhou and Xiangtan City, and other regions in Hunan were not suitable for double cropping super rice. These findings can provide valuable information for the large-scale cultivation of double cropping super rice in Hunan province.
2018, 24(2): 209-219.
doi: 10.16555/j.1006-8775.2018.02.009
Abstract:
Various features of the atmospheric environment affect the number of migratory insects, besides their initial population. However, little is known about the impact of atmospheric low-frequency oscillation (10 to 90 days) on insect migration. A case study was conducted to ascertain the influence of low-frequency atmospheric oscillation on the immigration of brown planthopper, Nilaparvata lugens (St?l), in Hunan and Jiangxi provinces. The results showed the following: (1) The number of immigrating N. lugens from April to June of 2007 through 2016 mainly exhibited a periodic oscillation of 10 to 20 days. (2) The 10-20 d low-frequency number of immigrating N. lugens was significantly correlated with a low-frequency wind field and a geopotential height field at 850 hPa. (3) During the peak phase of immigration, southwest or south winds served as a driving force and carried N. lugens populations northward, and when in the back of the trough and the front of the ridge, the downward airflow created a favorable condition for N. lugens to land in the study area. In conclusion, the northward migration of N. lugens was influenced by a low-frequency atmospheric circulation based on the analysis of dynamics. This study was the first research connecting atmospheric low-frequency oscillation to insect migration.
Various features of the atmospheric environment affect the number of migratory insects, besides their initial population. However, little is known about the impact of atmospheric low-frequency oscillation (10 to 90 days) on insect migration. A case study was conducted to ascertain the influence of low-frequency atmospheric oscillation on the immigration of brown planthopper, Nilaparvata lugens (St?l), in Hunan and Jiangxi provinces. The results showed the following: (1) The number of immigrating N. lugens from April to June of 2007 through 2016 mainly exhibited a periodic oscillation of 10 to 20 days. (2) The 10-20 d low-frequency number of immigrating N. lugens was significantly correlated with a low-frequency wind field and a geopotential height field at 850 hPa. (3) During the peak phase of immigration, southwest or south winds served as a driving force and carried N. lugens populations northward, and when in the back of the trough and the front of the ridge, the downward airflow created a favorable condition for N. lugens to land in the study area. In conclusion, the northward migration of N. lugens was influenced by a low-frequency atmospheric circulation based on the analysis of dynamics. This study was the first research connecting atmospheric low-frequency oscillation to insect migration.
2018, 24(2): 220-231.
doi: 10.16555/j.1006-8775.2018.02.010
Abstract:
Based on the three-pattern decomposition of global atmospheric circulation (TPDGAC), this study investigates the double-layer structure of the Hadley circulation (HC) and its interdecadal evolution characteristics by using monthly horizontal wind field from NCEP/NCAR reanalysis data from 1948-2011. The following major conclusions are drawn: First, the double-layer structure of the HC is an objective fact, and it constantly exists in April, May, June, October and November in the Southern Hemisphere. Second, the double-layer structure is more obvious in the Southern than in the Northern Hemisphere. Since the double-layer structure is sloped in the vertical direction, it should be taken into consideration when analyzing the variations of the strength and location of the center of the HC. Third, the strength of the double-layer structure of the HC in the Southern Hemisphere consistently exhibits decadal variations with a strong, weak and strong pattern in all five months (April, May, June, October, November), with cycles of 20-30 a and 40-60 a. Fourth, the center of the HC (mean position of the double-layer structure) in the Southern Hemisphere consistently and remarkably shifts southward in all the five months. The net poleward shifts over the 64 years are 5.18°, 2.11°, 2.50°, 1.79° and 5.76° for the five respective months, with a mean shift of 3.47°.
Based on the three-pattern decomposition of global atmospheric circulation (TPDGAC), this study investigates the double-layer structure of the Hadley circulation (HC) and its interdecadal evolution characteristics by using monthly horizontal wind field from NCEP/NCAR reanalysis data from 1948-2011. The following major conclusions are drawn: First, the double-layer structure of the HC is an objective fact, and it constantly exists in April, May, June, October and November in the Southern Hemisphere. Second, the double-layer structure is more obvious in the Southern than in the Northern Hemisphere. Since the double-layer structure is sloped in the vertical direction, it should be taken into consideration when analyzing the variations of the strength and location of the center of the HC. Third, the strength of the double-layer structure of the HC in the Southern Hemisphere consistently exhibits decadal variations with a strong, weak and strong pattern in all five months (April, May, June, October, November), with cycles of 20-30 a and 40-60 a. Fourth, the center of the HC (mean position of the double-layer structure) in the Southern Hemisphere consistently and remarkably shifts southward in all the five months. The net poleward shifts over the 64 years are 5.18°, 2.11°, 2.50°, 1.79° and 5.76° for the five respective months, with a mean shift of 3.47°.
2018, 24(2): 232-242.
doi: 10.16555/j.1006-8775.2018.02.011
Abstract:
By using the gauged rainfall in 160 stations within mainland China and the NCEP/NCAR reanalysis data, the impacts of anomalous SST in Kuroshio and its extension on precipitation in Northeast China were investigated. The results show that a difference in the meridional circulation such as the East Asia/Pacific teleconnection pattern (EAP) may be responsible for the difference in rainfall between 1998 and 2010. In comparison with 1998, the anomalous meridional circulation pattern in 2010 shifted northeastward, and then the western subtropical high, the mid-latitudinal trough and the northeastern Asia blocking high also shifted northeastward, causing intensified convergence of the cold and warm air masses at the southern region and thus more rainfall in the southwestern region and less in the northwestern region. In 1998, the anomalous cyclone, one component of the meridional pattern, located at the Songhuajiang-Nengjiang River basin, resulted in more rainfall in the majority of the area. The results of observation and the model show that the difference in SSTA in Kuroshio and its extension under the background of different El Niño events is the key point: (1) The anomalous warmth moved westward from the mid-Pacific to the east of the Philippine Sea during the central event, which led the heat resources shifting to the northeast in 2010; subsequently, a shift occurred to the north of the anomalous ascent and decent, followed by a warm SSTA in the region of Kuroshio’s extension in 2010 and Kuroshio in 1998. (2) The warm SSTA in the Kuroshio extension causing the Rossby wave activity flux strengthened in 2010, and then the westerly jet shifted northward and extended eastward. A warm SSTA in Kuroshio and cold SSTA in its extension in 1998 caused the westerly jet to shift southward and weaken. As a result, the anomalous anticyclone and cyclone shifted northward in 2010, and the blocking high also shifted northward.
By using the gauged rainfall in 160 stations within mainland China and the NCEP/NCAR reanalysis data, the impacts of anomalous SST in Kuroshio and its extension on precipitation in Northeast China were investigated. The results show that a difference in the meridional circulation such as the East Asia/Pacific teleconnection pattern (EAP) may be responsible for the difference in rainfall between 1998 and 2010. In comparison with 1998, the anomalous meridional circulation pattern in 2010 shifted northeastward, and then the western subtropical high, the mid-latitudinal trough and the northeastern Asia blocking high also shifted northeastward, causing intensified convergence of the cold and warm air masses at the southern region and thus more rainfall in the southwestern region and less in the northwestern region. In 1998, the anomalous cyclone, one component of the meridional pattern, located at the Songhuajiang-Nengjiang River basin, resulted in more rainfall in the majority of the area. The results of observation and the model show that the difference in SSTA in Kuroshio and its extension under the background of different El Niño events is the key point: (1) The anomalous warmth moved westward from the mid-Pacific to the east of the Philippine Sea during the central event, which led the heat resources shifting to the northeast in 2010; subsequently, a shift occurred to the north of the anomalous ascent and decent, followed by a warm SSTA in the region of Kuroshio’s extension in 2010 and Kuroshio in 1998. (2) The warm SSTA in the Kuroshio extension causing the Rossby wave activity flux strengthened in 2010, and then the westerly jet shifted northward and extended eastward. A warm SSTA in Kuroshio and cold SSTA in its extension in 1998 caused the westerly jet to shift southward and weaken. As a result, the anomalous anticyclone and cyclone shifted northward in 2010, and the blocking high also shifted northward.
2018, 24(2): 243-252.
doi: 10.16555/j.1006-8775.2018.02.012
Abstract:
Deviation exists between measured and simulated microwave radiometer sounding data. The bias results in low-accuracy atmospheric temperature and humidity profiles simulated by Back Propagation artificial neural network models. This paper evaluated a retrieving atmospheric temperature and humidity profiles method by adopting an input data adjustment-based Back Propagation artificial neural networks model. First, the sounding data acquired at a Nanjing meteorological site in June 2014 are inputted into the MonoRTM Radiative transfer model to simulate atmospheric downwelling radiance at the 22 spectral channels from 22.234GHz to 58.8GHz, and we performed a comparison and analysis of the real observed data; an adjustment model for the measured microwave radiometer sounding data was built. Second, we simulated the sounding data of the 22 channels using the sounding data acquired at the site from 2011 to 2013. Based on the simulated rightness temperature data and the sounding data, BP neural network-based models were trained for the retrieval of atmospheric temperature, water vapor density and relative humidity profiles. Finally, we applied the adjustment model to the microwave radiometer sounding data collected in July 2014, generating the corrected data. After that, we inputted the corrected data into the BP neural network regression model to predict the atmospheric temperature, vapor density and relative humidity profile at 58 high levels from 0 to 10 km. We evaluated our model’s effect by comparing its output with the real measured data and the microwave radiometer’s own second-level product. The experiments showed that the inversion model improves atmospheric temperature and humidity profile retrieval accuracy; the atmospheric temperature RMS error is between 1K and 2.0K; the water vapor density’s RMS error is between 0.2 g/m3 and 1.93g/m3; and the relative humidity’s RMS error is between 2.5% and 18.6%.
Deviation exists between measured and simulated microwave radiometer sounding data. The bias results in low-accuracy atmospheric temperature and humidity profiles simulated by Back Propagation artificial neural network models. This paper evaluated a retrieving atmospheric temperature and humidity profiles method by adopting an input data adjustment-based Back Propagation artificial neural networks model. First, the sounding data acquired at a Nanjing meteorological site in June 2014 are inputted into the MonoRTM Radiative transfer model to simulate atmospheric downwelling radiance at the 22 spectral channels from 22.234GHz to 58.8GHz, and we performed a comparison and analysis of the real observed data; an adjustment model for the measured microwave radiometer sounding data was built. Second, we simulated the sounding data of the 22 channels using the sounding data acquired at the site from 2011 to 2013. Based on the simulated rightness temperature data and the sounding data, BP neural network-based models were trained for the retrieval of atmospheric temperature, water vapor density and relative humidity profiles. Finally, we applied the adjustment model to the microwave radiometer sounding data collected in July 2014, generating the corrected data. After that, we inputted the corrected data into the BP neural network regression model to predict the atmospheric temperature, vapor density and relative humidity profile at 58 high levels from 0 to 10 km. We evaluated our model’s effect by comparing its output with the real measured data and the microwave radiometer’s own second-level product. The experiments showed that the inversion model improves atmospheric temperature and humidity profile retrieval accuracy; the atmospheric temperature RMS error is between 1K and 2.0K; the water vapor density’s RMS error is between 0.2 g/m3 and 1.93g/m3; and the relative humidity’s RMS error is between 2.5% and 18.6%.
2018, 24(2): 253-262.
doi: 10.16555/j.1006-8775.2018.02.013
Abstract:
In this study, the micro- and macro-physical properties, thermal structure and precipitation characteristics of cyclone eye walls and their surrounding spiral clouds were analysed with CloudSat and TRMM data for five tropical cyclones (TCs) in 2013. The results show that the ice-phase clouds of a mature TC are mainly above 5 km. With increasing altitude, the cloud droplet effective radius decreases, and the particle number concentration increases. Ice water content first increases and then decreases with increasing height. In the eye area, in addition to the well-known warm-core area, another warm core is also apparent around the eye at a height of 8 to 15 km. The horizontal distribution of precipitation is characterized by large-scale stratiform precipitation mixed with independent convective precipitation. The height of precipitation is mostly below 7.5 km, and the heavy rain is mainly below 5 km. When the peripheral convective clouds are strong enough, ice particles would be generated, thus providing conditions that are favourable for the formation of precipitation below.
In this study, the micro- and macro-physical properties, thermal structure and precipitation characteristics of cyclone eye walls and their surrounding spiral clouds were analysed with CloudSat and TRMM data for five tropical cyclones (TCs) in 2013. The results show that the ice-phase clouds of a mature TC are mainly above 5 km. With increasing altitude, the cloud droplet effective radius decreases, and the particle number concentration increases. Ice water content first increases and then decreases with increasing height. In the eye area, in addition to the well-known warm-core area, another warm core is also apparent around the eye at a height of 8 to 15 km. The horizontal distribution of precipitation is characterized by large-scale stratiform precipitation mixed with independent convective precipitation. The height of precipitation is mostly below 7.5 km, and the heavy rain is mainly below 5 km. When the peripheral convective clouds are strong enough, ice particles would be generated, thus providing conditions that are favourable for the formation of precipitation below.
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