Vol.22, NO.1, 2011

Display Method:
Physical Basis of Short-term Climate Prediction in China and Short-term Climate Objective Prediction Methods
Wei Fengying
2011, 22(1): 1-11.
Short-term climate prediction means forecasting monthly, seasonal, and inter-annual climate trends in the future based on the principles of atmospheric science by using climate dynamics, statistics and other means. Although many researches on the short-term climate prediction have been implemented, the prediction capability is far from meeting the demands of the national economy development. A brief review of the progress in China has been made on the short-term climate prediction in recent 60 years. The physical basis of short-term climate prediction, including the external forcing and internal characteristics of the atmosphere is described. Several new short-term climate objective prediction methods are proposed and their applications to operational prediction are introduced.The ElñNio/La Niña phenomenon and snow cover over the Tibetan Plateau in winter are two important external forcing factors for short-term prediction in China. The large-scale atmospheric circulation patterns, for example, North Atlantic Oscillation (NAO), North Pacific Oscillation (NPO), Southern Oscillation (SO) and Arctic Oscillation (AO), are also important physical foundation for short-term climate prediction. Since the 1990s, numerical climate models have made rapid progress, and statistical downscaling method is also an important way to improve the accuracy of short-term climate prediction. In recent years, role of inter-decadal variability in short-term climate prediction are also attracting great attentions.
Synoptic Scale and Mesoscale Characteristics of 7·17 Urumqi Heavy Rainfall in 2007
Kong Qi, Zheng Yongguang, Chen Chunyan
2011, 22(1): 12-22.
Xinjiang is located in a semi-arid area, but a heavy rainfall event occurs unexpectedly along the Tianshan Mountains in Xinjiang during 13—18 July 2007. The circulation and the persistent mechanism of the low vortex as well as the mesoscale characteristics for the heavy rainfall in Urumqi during 16—17 July 2007 are analyzed and compared with one similar case in the same period of 1996, by use of the 1-hour and 1-minute precipitation data, geostationary satellite images, conventional surface and upper data, NCEP 1°×1° reanalysis data and the new generation Doppler radar data. The results are as follows: The heavy rainfall takes place under the favorable large-scale circumfluence. It is a large scale heavy rainfall which is related to the baroclinic disturbance. The central-Asian vortex is the major influential system, but the location, form and intensity are different from those of the cases in 1996. The atmospheric stratification turns unstable, but it is weaker comparing with the heavy rainfall in the eastern part of China. The characteristics of the stability are different during different rainfall periods, exhibiting the pattern of both stratiform and convective precipitation. The intrusion of the cold and dry air strengthens the instability of the atmosphere, and plays an important role for the heavy rainfall. The long maintenance of the vortex after the landing in Xinjiang is associated with the high PV anomaly (dry and cold air intrusion) in the upper-middle troposphere. Strong moisture convergence develops rapidly at lower level and the water mainly comes from the eastern part of the Tibetan Plateau, western part of Gansu Province and the north of the Southern Xinjiang Basin. The satellite and radar images show that there are obvious meso-γ-scale convective rainy cluster with characteristics of the echo pendency, vertical wind shear, but they are much weaker than those heavy rainfall systems in the east region of China. Radar radial base velocity products reveal that the mesoscale radial convergence may be the important trigger mechanism for the mesoscale convective rain cluster. Compared with the rainfall in eastern China, the rainfall in Xinjiang shows the characteristics of both synoptic scale and mesoscale. The water sources are less abundant and the convergence is more important. The instability of atmospheric stratification is not so strong without obvious low-level jet. The intrusion of the cold and dry air comes from the middle level of the troposphere. The mesoscale convective rainy-clusters have the characteristics of the mesoscale convective clouds, but the convection is not too intense as the cloud top black body temperature of the cloud clusters is not too low, and the vertical height of strong echo is not too high, either.
The Structure and Origin of a Rainstorm-inducing Mesoscale Convective System on Western Coast of Bohai Bay
Yi Xiaoyuan, Li Zechun, Sun Xiaolei, Liu Yiwei, Sun Mina, Zhu Leilei
2011, 22(1): 23-34.
Black day phenomenon and sudden hard rain occur in Tianjin on 16 June 2009. Based on several monitoring data such as FY-2 satellites data, multi-radar composite and intensive automatic stations data, combing with VDRAS data, the origins of black day and the rainstorm are analyzed. The thermal and dynamical structure which leads to the occurrence and development of meso-β-scale, meso-γ-scale convective systems in circular meso-α-scale convective system are also studied. 31 circle-shape MCSs which lead to severe weather on western coast of Bohai Bay in 2004—2009 are preliminarily sorted and summed up in size and life-circle.Less than 16% circular MαCS on the western coast of Bohai Bay develop into MCC with no more than 15×104 km2 large (where the TBB is equal to or below-52 ℃). The MCCs generally last no more than 8 hours and always happen in night. But in the South China, it's common to see MCC larger than 20×104 km2 which last more than 10 hours."6.16" MαCS has special characteristics in range, time of occurrence and precipitation echoes on the thickness, which are the main causes for black day phenomenon. New MβCS and MγCS develop constantly in the west of MαCS move eastward into the high-energy region with warm-moisture intensively and maintain, leading to rainstorm in Tianjin.Cold air intrudes into MαCS from its back at the height of 1.3—2.4 km, flows out from rain echoes convergence line or close-gradually line, and triggers the development of MβCS with southwestern warm-moisture flow.In the ascending center of MαCS, the vertical velocity is 0.7 Pa·s-1 at height of 500 hPa. Below the height of 700 hPa (about 3 km), ascending vertical velocity reaches 1.8 m·s-1, and each of MβCS1—3 has independent vertical circle. Under the height of 1 km, there is corresponding boundary layer circulation for MβCS1—3. With evolution of MβCS1—3, cold pool (the areas of negative perturbation temperature) appears under the height of 2 km and the area of positive perturbation temperature appears above it, so the vertical structure is stable.
Calculation and Validation Method of Cloud Amount by High Spatial Resolution Satellite Data
Liu Jian, Zhang Liyang
2011, 22(1): 35-45.
Cloud plays an important role in earth-atmosphere radiation balance system, atmospheric circulation and climate change. Surface observation is a regular method to obtain cloud amount data but it is limited by time and place. International Satellite Cloud Climatology Project (ISCCP) offers cloud parameters product with better quality, but the best spatial resolution is just 30 km. Based on re-calibration and accurate re-location to NOAA daily data during 1998—2008, total cloud amount are calculated with improved cloud detection and radiation calculation method, and validated by ISCCP and surface regular observation data. The temporal and spatial resolution (daily and 0.01°×0.01°) of this cloud amount data is much better than ISCCP product. The sub cloud pixel covered problem is also resolved. Compared with ISCCP DX cloud detection data, validation result shows that clear pixel detection consistence reaches 0.70, cloud pixel detection consistence reaches 0.60, and total cloud detection consistence reaches 0.57. For cloud amount, the coefficient between the calculated cloud amount and surface observation is higher than 0.70. The main differences between cloud amount of ISCCP and calculated data come from two aspects. First, ISCCP method doesn't consider sub-pixel problem reasonably. If one pixel is covered by cloud, ISCCP method regards its cloud amount as one while with the radiation calculation method, clear and completely cloudy cover radiation is calculated, and then every pixel cloud amount according to its radiation value is calculated. Second, different spatial resolution and targets influence the evaluation of the two sets of data. Limited by observation angles and time, ground and satellite observations are not the same. The validation shows that the calculated long time series cloud parameters with high temporal and spatial resolution have good quality, and could play important role in weather analysis and climate change research.
Retrieving Precipitable Water Vapor Based on the Near-infrared Data of FY-3A Satellite
Hu Xiuqing, Huang Yibin, Lu Qifeng, Zheng Jing
2011, 22(1): 46-56.
The technique of retrieving precipitable water vapor (PWV) based on near-infrared (NIR) data of Medium Resolution Spectral Imager (MERSI) on board FY-3A satellite is introduced. Five NIR channels are designed on the MERSI instrument for PWV observation, three of which are water vapor absorption channels centered near 905 nm, 940 nm and 980 nm respectively and others are atmospheric window channels at 865 nm and 1030 nm. The method adopted here for PWV retrieval is based on the ratio of reflected solar radiance (or apparent reflectance) detected by satellite between water vapor absorption channels and atmospheric window channels. By employing channel ratios, the aerosol extinction distribution and the variation effect of surface reflectance are partially removed, and the atmospheric transmittance of water vapor channels is approximately obtained. The PWV is derived from the atmospheric transmittance based on a Look-up Table which is pre-calculated using a radiation transfer model. The sensitivities of atmospheric transmission in each NIR water vapor channels of MERSI to the total precipitable water vapor are also simulated. It is found that 905 nm channel is more sensitive under humid conditions while the strong absorption channel at 940 nm is sensitive under dry conditions. And the two weak absorption channels have similar sensitivity to total water vapor amount. In this case, under a given atmosphere condition, the derived PWV values from three water vapor channels may be a little different. The weighted average of three derived PWV values is regarded as the final PWV product and the weighing coefficients are determined by their sensitivity.The procedure of the operational PWV product generation is designed and conducted for experimental retrieval. Based on the global data of MERSI, FY-3A Products Generation System (PGS) can successfully generate the daily global and regional PWV L2 products and multi-day integrated L3 products, which can clearly display the spatial distribution of water vapor amounts over global land area. The result indicates that FY-3A/MERSI has an excellent ability in detecting NIR water vapor, and can demonstrate fine characteristic of PWV spatial distributions. As 940 nm channel shows good application under dry atmosphere conditions and 905 nm or 980 nm channel work well under humid situation, acceptable retrieval accuracy can always be achieved by combining these channels. In order to assess the accuracy, the retrieved PWV from MERSI NIR are compared with the ground-based sounding data. Over cloud free area, there is a good agreement between them in variation trend and spatial distribution. The MERSI PWV results are steady but 20%—30% lower than sounding, so the retrieval algorithm and the Look-up Table need to be updated to reduce this bias in the near future.
The Influence of the Subtropical Sea Surface Temperature over the Western Pacific on Spring Persistent Rains
Zhang Bo, Zhong Shanshan, Zhao Bin, He Jinhai, Chen Longxun
2011, 22(1): 57-65.
Using the Community Atmospheric Model Version 3.1 (CAM3.1) provided by National Center for Atmospheric Research (NCAR), the influence of the East Asian subtropical zonal land-sea thermal difference on the spring persistent rains is studied. The results show that the monthly sea surface temperature over the western Pacific (15°—35°N, 120°—150°E) are two months ahead of schedule, the seasonal conversion of the East Asia—the western Pacific subtropical zonal land-sea thermal difference is delayed, and the thermal difference between the East Asia and the western Pacific in spring is decreased. Under this condition, the intensity of the southwest winds at 850 hPa over East China in March and April decreases, and the rainfall over the region to south of 30°N decreases during the period from March to April, the remarkable decreasing periods are mid-March and mid-late April. The result shows that the intensity of spring persistent rains decreases due to the little land-sea thermal difference. The important role of the East Asian subtropical zonal land-sea thermal difference on the spring persistent rains over Southeastern China is verified. As far as the mechanisms are concerned, the results are as follows.When the land-sea thermal difference of subtropical zonal is minished, the intensity of vortex over the southeastern Tibetan Plateau weakens and then the geopotential difference between this vortex and the western Pacific subtropical high minishes. The western Pacific subtropical high over the middle and low latitudes weakens, and the intensity of the southeast wind decreases over the region to north of the western Pacific subtropical high. Therefore, the southeast warm moist airflows decrease and the convergence intensity of the moisture flux divergence weakens. Under this general circulation conditions, there are no heavy spring persistent rains.
The Annual Frequency Prediction of Tropical Cyclones Affecting China
Ying Ming, Wan Rijin
2011, 22(1): 66-76.
Seasonal prediction schemes are developed for the annual frequency of tropical cyclones (TCs) affecting China, which are more practicable than predicting the genesis frequency for disaster mitigation. The frequencies of TCs affecting the whole China, East China and South China are identified by using the China Meteorological Administration TC-induced wind and precipitation data under one of the three criteria: The storm precipitation heavier than 50 mm has been observed at more than one station in the region; the sustained wind severer than Beaufort scale 7 or wind gusts larger than Beaufort scale 8 has been observed at more than one station in the region; the storm precipitation heavier than 30 mm, and either the sustained wind severer than Beaufort scale 6 or wind gusts larger than Beaufort scale 7 has been observed at more than one station in the region. Seasonal prediction schemes are then developed for these TC frequencies (TCFs) according to their lag correlations with the sea surface temperature (SST) and atmospheric variables during the period of 1961—2000. The NOAA ER SST and the NCEP/NCAR reanalysis data, including sea level pressure, geopotential height at 200, 500 hPa and 850 hPa, and both the zonal and meridional components of wind vectors at 200, 500 hPa and 850 hPa, are used to derive the predictors. For better representing the variation of the circulation systems in three dimensions, the predictor series are constructed by averaging data within those adjoining significant areas of correlation at various levels in each month. For the frequencies of TCs affecting the South and East China, respectively, analyses on the predictors suggest that their predictors of previous autumn and winter are quite consistent with each other; however, their predictors of previous spring show more differences. For each model, the colinearity among the predictors, including data since 2001, is reduced by applying the Principal Component Analysis approach, and the optimal subset regression model is then developed based on those derived independent predictors. All prediction schemes for TCFs are validated using the data of 2001—2008 and the results indicate that all schemes show skills in predicting frequencies of TCs affecting China though they still can be further improved.
Prediction of Monthly Precipitation and Number of Extreme Precipitation Days with Statistical Downscaling Methods Based on the Monthly Dynamical Climate Model
Liu Lüliu, Sun Linhai, Liao Yaoming, Du Liangmin, Li Xiang
2011, 22(1): 77-85.
The prediction of precipitation especially extreme precipitation is important but difficult. Dynamical climate models play important roles in the climate prediction and show good skills in large-scale circulation prediction. However, its prediction skill of daily precipitation is limited on regional or smaller spatial scale. So dynamical or statistical downscaling is developed to provide prediction with high resolution. Statistical downscaling can make full use of the large-scale circulation information with high skill of global climate model, and simulate everyday climate variables on the regional or point scale. It has become a popular method in climate prediction and climate change research.Dynamical Extension Regional Forecast Model (DERF) by National Climate Center, CMA has been used in the climate prediction for nearly ten years. Like other global climate models, it has good skills in predicting circulation fields such as height, wind, and sea level pressure. Optimum subsets regression (OSR) is used to predict precipitation anomaly at 133 stations in China for 6 periods (1—10 days, 11—20 days, 21—30 days, 31—40 days, 1—30 days, 11—40 days) using geopotential height, zonal wind, meridional wind and sea level pressure as predictors by DERF. The OSR models are verified with cross validation method using data from 1982 to 2006. Five operational sores (Ratc, CLTc, P, ACC and TS) are compared with the results directly forecasted by DERF. The results show that OSR can improve prediction skill to different extents, especially for 11—40 days. Then two statistical downscaling methods are used to predict number of extreme precipitation days. One is predicting directly as predictant with OSR method using large circulations from DERF as predictors (named as 1-step method), which is similar to precipitation anomaly prediction. The other one is to compute the day number using simulation results of weather generator (WG) under the condition of precipitation anomaly predicted by OSR downscaling (named as 2-step method). Random prediction is compared with the two methods. Crossing verification from 1982 to 2006 show that the predict skill of the two statistical methods is better than that of random prediction. The skill of 2-step method is better than 1-step method to predict number of extreme precipitation days in winter, but worse in summer. It can be concluded that the methods of OSR and combination of OSR and WG have high skill to predict precipitation and number of extreme precipitation days. The prediction information can provide important information for short-range climatic prediction.
Application of MOS Method on Pentad Mean Temperature Prediction in Dynamical Extended Range
Chen Yuying, Chen Nan, Wang Suyan, Shao Jian, Mu Jianhua, Na Li
2011, 22(1): 86-95.
Stepwise regression MOS statistical method is applied to predict the pentad mean temperature of future 40 days at 24 weather stations in Ningxia, using the pentad mean temperature and dynamical extended range forecasting products from January 1982 to March 2010. In order to evaluate the predicting capability of the direct numerical model output products (DMO) and MOS which use the seasonal and monthly data, the prediction results of DMO and MOS from January to March in 2009 and 2010 are compared.The prediction accuracy and stability of MOS improved remarkably comparing with DMO, MOS method can predict the trend and extent of violent weather changes in temperature. With the drawing near of predicting time and successive correction, its prediction error values decreases gradually, and predicting results can be for the reference of medium term prediction operation.MOS prediction capability is different when using data of different lengths. The prediction capability of MOS using monthly data is better, because the chosen prediction factors using monthly data can better indicate the correlation of prediction objects in this period, and its physical meaning is much distinct. So more monthly data samples lead to better temperature prediction result and stability.MOS method merely applies the output products of dynamical extended range prediction model, so its prediction results thoroughly rely on the accuracy and stability of numerical prediction model. The prediction results may be more accurate if some observation factor, local experimental factor and climatic factor are added. There are 4 aspects which need attentions when establishing MOS equations: The equations should be established based on experimental calculating error values; the establishment of F-test value at each weather station should also consider experimental calculating error values; using data samples of longer temporal scales to verify if MOS prediction results will be better using more monthly data samples; finally, more new data are recommended to improve MOS prediction capability in the future.
The Evaluation of WSR-88D Hail Detection Algorithm over Guizhou Region
Wang Jin, Liu Liping
2011, 22(1): 96-106.
The evaluation databases for the WSR-88D hail detection algorithm have been built by using hail observation data of 504 hail prevention spots in Guizhou and Doppler radar data of Guiyang during 8 of severe hail cases from 2005 to 2006, filtered by specific conditions including definition of severe hail, observation range along the cell track, and time-window methodology, etc. These conditions make the databases provide a more accurate picture of algorithm performance. The algorithms are evaluated using the probability of detection (DPO), false alarm ratio (RFA), and critical success index (ICS) statistics.It shows that POH (probability of hail) threshold of 50% get the highest RFA, and different POH thresholds get similar ICS, suggesting that it is unreliable to use POH as the only parameter for hail detection in Guizhou region. The difference of climatology between Guizhou area and central Switzerland where the initial POH curve is derived is the crucial cause why POH algorithm becomes unreliable and gets higher RFA.Assigning POSH (probability of severe hail) 30% leads to the highest ICS score in Guizhou region, but this threshold does not always get the best performance in these 8 severe hail cases. The difference of WTSM (Warning Threshold Selection Model) in different climatic region is the main cause why the default POSH algorithm gets a bad performance. An improved WTSM will predict the optimum ISH threshold for each day more accurately. It will help ensure that the POSH threshold of 50% always corresponds to the largest possible ICS every day. The re-evaluation of the improved POSH algorithm shows that it has decreased the hail detection RFA, and gets a higher performance of severe hail detection in Guizhou.
Decision Tree Forecasting Models of Sea Fog for the Coast of Guangdong Province
Huang Jian, Huang Huijun, Huang Minhui, Xue Dengzhi, Mao Weikang, Bai Yujie
2011, 22(1): 107-114.
Sea fog is a phenomenon of water vapor condensation or sublimation in marine atmospheric boundary layer and is also one of the main disastrous weathers on the coast of Guangdong Province in spring. However, there is no suitable method for operational sea fog forecasting in Guangdong due to the complexity of physical processes involved in the formation of sea fog. Therefore, historical sea fog reports from Shantou, Zhuhai and Zhanjiang surface meteorological observation and NCEP/NCAR FNL reanalysis for the period of 2000—2008 are analyzed to explore the feasibility of sea fog forecasting with a 24-hour lead time. The relationship between marine atmospheric conditions and sea fog events is examined by Classification and Regression Trees (CART), employing the NCEP/NCAR reanalysis data 24 hours before the sea fog events. Then, the decision tree models for sea fog forecasting are developed based on results of classification analysis. Finally, the physical significance of the forecasting rules is discussed based on existing theoretical knowledge on sea fog.The validation results by 10 cross-validation show that the forecasting accuracy of sea fog decision tree models developed by CART can reach 83.7%, 73.7% and 82.4% respectively for Shantou, Zhuhai and Zhanjiang on the coast of Guangdong Province. It can be interpreted or understood easily due to the clear logical relationship. The decision-making procedure can be developed and used directly to make fog/no-fog identification in operational sea fog forecasting with clear physical meanings. It also reflects the importance of the water vapor and the cooling effect of cold sea surface in the formation of advective cooling fog well. Simple calculation processes and relatively high classification accuracy make the CART an effective tool to develop sea fog forecasting model.
MCS Identification and Tracking Based on Geo-satellite IR Images
Fei Zengping, Wang Hongqing, Zhang Yan, Song Shuai, Liu Jiajun, Zheng Yongguang
2011, 22(1): 115-122.
MCS (mesoscale convection systems) are significant weather systems causing heavy rain, hail and other severe weather events. Many disastrous weathers are usually caused by strong convection systems of 10—200 kilometers, but they are very difficult to forecast in operation. Geostationary satellite infrared imagery with higher spatial and temporal resolution provides much practical information for identifying and tracking MCS automatically from a broader perspective. Many researches are implemented on MCS based on geostationary satellite infrared imagery, amending the MCS judgment standard according to the actual condition of the weather. However, the lack of mature auto-tracking software has limited the extensive surveying of MCS using geostationary satellite. Artificial method is too onerous and error prone.An automatic method of identifying, saving, tracking and characteristics recording has been established based on imagery processing and time series analyzing. First, smooth sharp noise of the satellite image with mean filtering method and median filtering method. Then binary convert the preprocessed images, identify a MCS cloud regiment by marking and extracting the characteristic quantity, and get each target cloud regiment of time sequence. By computing the possible position, the target MCS, is checked if its characteristic matches with the stored information in area, strength, etc. Thus, the time sequence of the MCS cloud regiment is tracked automatically. The method is applied in MCS identifying, tracking automatically with characteristic statistics during the flood over Huaihe River in 2003, and the validation results show that this method has the ability of identifying MCS quickly each time, as well as tracking MCS of multi-time effectively.
The Application of Multi-source Data to Three-dimensional Cloud Amount Analysis in LAPS
Liu Ruixia, Chen Hongbin, Shi Chunxiang, Zhang Xiaohu
2011, 22(1): 123-128.
The quantitative three-dimensional cloud data is important in nowcasting and the modeling of weather and climate. Therefore, 5 schemes are designed to construct three-dimensional cloud amount data from FY-2C satellite data, radar data, ground observation data using LAPS (Local Analysis Prediction System) developed by NOAA ERSL. The roles of each data in LAPS system are also analyzed. Scheme 1 uses background data only, and Scheme 2 adds ground observation data. Scheme 3 employs background data and FY-2C satellite data, Scheme 4 uses background data and radar data, and Scheme 5 takes background data, ground observation data, radar data and FY-2C satellite data into consideration.The analysis indicates that every data is important in order to get more objective three-dimensional cloud distribution. Ground observation data gives information of cloud base and cloud amount for the lower atmosphere. Satellite infrared brightness temperature and visible reflectance provide cloud top height and cloud amount in the upper atmosphere. Radar data can help to construct three-dimensional cloud field in the middle and lower level. Combining all these data can provide more objective information of three-dimensional cloud amount.Comparing column cloud amount deduced by LAPS with satellite visible and infrared image shows that the cloud distribution when assimilating all these data is more consistent with real situation. Moreover, the satellite data is one of the most important data in cloud analysis in LAPS.