How effective is the new generation of GPM satellite precipitation in characterizing the rainfall variability over Malaysia?

We investigated the potential of the new generation of satellite precipitation product from the Global Precipitation Mission (GPM) to characterize the rainfall in Malaysia. Most satellite precipitation products have limited ability to precisely characterize the high dynamic rainfall variation that occurred at both time and scale in this humid tropical region due to the coarse grid size to meet the physical condition of the smaller land size, sub-continent and islands. Prior to the status quo, an improved satellite precipitation was required to accurately measure the rainfall and its distribution. Subsequently, the newly released of GPM precipitation product at half-hourly and 0.1° resolution served an opportunity to anticipate the aforementioned conflict. Nevertheless, related evidence was not found and therefore, this study made an initiative to fill the gap. A total of 843 rain gauges over east (Borneo) and west Malaysia (Peninsular) were used to evaluate the rainfall the GPM rainfall data. The assessment covered all critical rainy seasons which associated with Asian Monsoon including northeast (Nov. - Feb.), southwest (May - Aug.) and their subsequent inter-monsoon period (Mar. - Apr. & Sep. - Oct.). The ability of GPM to provide quantitative rainfall estimates and qualitative spatial rainfall patterns were analysed. Our results showed that the GPM had good capacity to depict the spatial rainfall patterns in less heterogeneous rainfall patterns (Spearman’s correlation, 0.591 to 0.891) compared to the clustered one (r = 0.368 to 0.721). Rainfall intensity and spatial heterogeneity that is largely driven by seasonal monsoon has significant influence on GPM ability to resolve local rainfall patterns. In quantitative rainfall estimation, large errors can be primarily associated with the rainfall intensity increment. 77% of the error variation can be explained through rainfall intensity particularly the high intensity (> 35 mm d^-1). A strong relationship between GPM rainfall and error was found from heavy (~35 mm d^-1) to violent rain (160 mm d^-1). The output of this study provides reference regarding the performance of GPM data for respective hydrology studies in this region.     

Validation of the Satellite-Derived Rainfall Estimates over the Tibet

Measuring rainfall from space appears to be the only cost effective and viable means in estimating regional precipitation over the Tibet, and the satellite rainfall products are essential to hydrological and agricultural modeling. A long-standing problem in the meteorological and hydrological studies is that there is only a sparse raingauge network representing the spatial distribution of precipitation and its quantity on small scales over the Tibet. Therefore, satellite derived quantitative precipitation estimates are extremely useful for obtaining rainfall patterns that can be used by hydrological models to produce forecasts of river discharge and to delineate the flood hazard area. In this paper, validation of the US National Oceanic and Atmospheric Administration (NOAA) Climate Prediction Center (CPC) RFE (rainfall estimate) 2.0 data was made by using daily rainfall observations at 11 weather stations over different climate zones from southeast to northwest of the Tibet during the rainy season from 1 June to 30 September 2005 and 2006. Analysis on the time series of daily rainfall of RFE-CPC and observed data in different climate zones reveals that the mean correlation coefficients between satellite estimated and observed rainfall is 0.74. Only at Pali and Nielamu stations located in the southern brink of the Tibet along the Himalayan Mountains, are the correlation coefficients less than 0.62. In addition, continuous validations show that the RFE performed well in different climate zones, with considerably low mean error (ME) and root mean square error (RMSE) scores except at Nielamu station along the Himalayan range. Likewise, for the dichotomous validation, at most stations over the Tibet, the probability of detection (POD) values is above 73% while the false alarm rate (FAR) is between 1% and 12%. Overall, NOAA CPC RFE 2.0 products performed well in the estimation and monitoring of rainfall over the Tibet and can be used to analyze the precipitation pattern, produce discharge forecast, and delineate the flood hazard area.     

Large-scale precipitation estimation using Kalpana-1 IR measurements and its validation using GPCP and GPCC data

Large-scale precipitation estimation is very important for climate science because precipitation is a major component of the earth's water and energy cycles. In the present study, the GOES precipitation index technique has been applied to the Kalpana-1 satellite infrared (IR) images of every three-hourly, i.e., of 0000, 0300, 0600,??., 2100?hours UTC, for rainfall estimation as a preparatory to the INSAT-3D. After the temperatures of all the pixels in a grid are known, they are distributed to generate a three-hourly 24-class histogram of brightness temperatures of IR (10.5?C12.5???m) images for a 1.0°?×?1.0° latitude/longitude box. The daily, monthly, and seasonal rainfall have been estimated using these three-hourly rain estimates for the entire south-west monsoon period of 2009 in the present study. To investigate the potential of these rainfall estimates, the validation of monthly and seasonal rainfall estimates has been carried out using the Global Precipitation Climatology Project and Global Precipitation Climatology Centre data. The validation results show that the present technique works very well for the large-scale precipitation estimation qualitatively as well as quantitatively. The results also suggest that the simple IR-based estimation technique can be used to estimate rainfall for tropical areas at a larger temporal scale for climatological applications.     

Sub-daily scale validation of satellite-based high-resolution rainfall products

Recent research efforts have been geared towards developing high-resolution rainfall products from satellites for hydrological applications. A necessary step in assessing the potential and utility of these products is to quantify the uncertainty associated with them at validation scales appropriate for hydrological applications. The main objective of this paper is to evaluate the accuracy of the widely-known PERSIANN-CCS high-resolution (hourly, 0.04° × 0.04°) satellite rainfall products against high-quality NEXRAD radar rainfall observations in the Little Washita watershed. Our results reveal that (1) PERSIANN-CCS shows high skills in reproducing the patterns of inter-annual rainfall variability on a monthly basis; (2) both at the hourly and storm scales, the performance statistics of PERSIANN-CCS exhibit large spread, suggesting that the quality of PERSIANN-CCS product is almost unique for each hour and storm; and (3) significant improvement in performance statistics is obtained as PERSIANN-CCS products are averaged to longer sub-daily time scales. The implications of our results are: (1) PERSIANN-CCS could be used with high confidence for inter-annual rainfall variability studies; (2) PERSIANN-CCS products need to be accompanied by corresponding hourly error estimates in order to provide meaningful error estimates for hydrological applications; and (3) research is needed to characterize the tradeoff between the quality of rainfall input and the space-time resolution of hydrological modeling, as a function of watershed size and hydrologic model complexity level.     

Influence of spatial and temporal scales on statistical analyses of rainfall variability in the River Nile basin

In this study, empirical orthogonal function was applied to analyze rainfall variability in the Nile basin based on various spatio-temporal scales. The co-occurrence of rainfall variability and the variation in selected climate indices was analyzed based on various spatio-temporal scales. From the highest to the lowest, the cumulative amount of variance explained by the first two principal components (PCs) for any selected size of the spatial domain was obtained for the annual, seasonal, and monthly rainfall series respectively. The variability in the annual rainfall of 1° × 1° spatial coverage explained by only the first PC was about 55% on average. However, this percentage reduced to about 40% on average across the study area when the size of the spatial domain was increased from 1° × 1° to 10° × 10°. The variation in climate indices was shown to explain rainfall variability more suitably at a regional than location-specific spatial scale. The magnitudes and sometimes signs of the correlation between rainfall variability and the variation in climate indices tended to vary from one time scale to another. These findings are vital in the selection of spatial and temporal scales for more considered attribution of rainfall variability across the study area.     

Validation of satellite rainfall products over Greece

Six widely available satellite precipitation products were extensively validated and intercompared on monthly-to-seasonal timescales and various spatial scales, for the period 1998–2006, using a dense station network over Greece. Satellite products were divided into three groups according to their spatial resolution. The first group had high spatial (0.5°) resolution and consists only of Tropical Rainfall Measuring Mission (TRMM) products: the TRMM Microwave Imager (TMI) precipitation product (3A12) and the TRMM multisatellite precipitation analysis products (3B42 and 3B43). The second group comprised products with medium spatial (1°) resolution. These products included the TRMM 3B42 and 3B43 estimates (remapped to 1° resolution) and the Global Precipitation Climatology Project one-degree daily (GPCP-1DD) analysis. The third group consisted of low spatial (2.5°) resolution products and included the 3B43 product (remapped to 2.5° resolution), the GPCP Satellite and Gauge (GPCP-SG) product, and the National Oceanographic and Atmospheric Administration Climate Prediction Center (NOAA-CPC) Merged Analysis (CMAP). Rain gauge data were first gridded and then compared with monthly and seasonal precipitation totals as well as with long-term averages of the six satellite products at different spatial resolutions (2.5°, 1°, and 0.5°). The results demonstrated the excellent performance of the 3B43 product over Greece in all three spatial scales. 3B42 from the first and second group and CMAP from the third exhibited a reasonable skill.     

Evaluating spatial scales of climate variability in sub-Saharan Africa

Summary Spatial scales of variability in seasonal rainfall over Africa are investigated by means of statistical and numerical techniques. In the statistical analysis spatial structure is studied using gridded 0.5° resolution monthly data in the period 1948–1998. The de-seasonalized time series are subjected to successive principal component (PC) analysis, allowing the number of modes to vary from 10 to 24, producing cells of varying dimension. Then the original rainfall data within each cell are cross-correlated (internal), then averaged and compared with the adjacent cells (external) for each PC solution. By considering the ratio of internal to external correlation, the spatial scales of rainfall variability are evaluated and an optimum solution is found whose cell dimensions are approximately 10⁶ km². The aspect of scale is further studied for southern Africa by consideration of numerical model ensemble simulations over the period 1985–1999 forced with observed sea surface temperatures (SSTs). The hindcast products are compared with observed January to March (JFM) rainfall, based on a station-satellite merged analysis of precipitation (CMAP) data at 2.5° resolution. Validations for different sized areas indicate that cumulative standardized errors are greatest at the scale of a single grid cell (10⁴ km²) and decrease 20–30% by averaging over successively larger areas (10⁶ km²).     

GPM卫星和地面雷达对江苏盐城龙卷风强降水估测的对比

为综合评估卫星和天气雷达在2016年6月23日盐城龙卷风期间的强降水过程的降水估测精度,以国家级雨量站观测数据为基准,结合相关系数(CC)、相对误差(RB)、均方根误差(RMSE)以及分级评分指标,利用S波段的天气雷达定量降雨估测产品(RQPE)和全球降水观测计划多卫星融合产品(IMERG_FRCal,IMERG_FRUncal,IMERG_ERCal)进行比较。结果表明,雷达和卫星的累积降水量与雨量站的空间相关性很强(相关系数大于0.9),基本上能捕捉到整个降水过程的空间分布。降水主要分布在江苏省北部,但卫星高估了江苏省东北部强降水中心的降水量;对于小时时序区域平均降水,卫星高估了降水,而雷达低估了累积降水量。综合降水中心区域分析,IMERG的强降水区域降水量与雨量站的时间序列的偏差显著;RQPE在降水峰值达到之前及峰值之后与地面雨量站的变化趋势基本一致,但对降雨量峰值有明显的偏低。RQPE能较为准确地在时间上捕捉到降雨强度的变化趋势,但对于大雨及暴雨的估测能力不佳;RQPE的POD、SCI值都远远高于IMERG, FAR也较小。IMERG几乎未能监测到强降水的发生。总体上,RQPE对此次龙卷风强降水量的估测表现优于3种IMERG产品,特别是在捕捉强降水区域的空间分布方面,但对于强降水的估测能力仍需进一步改善。     

Validation of TRMM-3B42 precipitation product over the tropical Indian Ocean using rain gauge data from the RAMA buoy array

In the present study, an attempt has been made to validate the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA)-3B42 recently released version 7 product over the tropical Indian Ocean using surface rain gauges from the National Oceanic and Atmospheric Administration/Pacific Marine Environmental Laboratory Research Moored Array for African–Asian–Australian Monsoon Analysis and Prediction buoy array available since late 2004. The validation exercise is carried out at daily scale for an 8-year period of 2004–2011. Results show statistically significant linear correlation between these two precipitation estimates ranging from 0.40 to 0.89 and the root-mean-square error varies from about 1 to 22 mm day^?1. Although systematic overestimation of precipitation by the TMPA product is evident over most of the buoy locations, the TMPA noticeably underestimates higher (more than 100 mm day^?1) and light (less than 0.5 mm day^?1) precipitation events. The highest correlation is observed during the southwest monsoon season (June–September) even though bias is the maximum possibly due to relatively lower fraction of stratiform precipitation during the monsoon season than other seasons. Furthermore, the TMPA estimates slightly underestimate or misses intermittent warm precipitation events as compared to the precipitation radar derived precipitation rates.     

广西TRMM降雨产品多时间尺度精度评估

集合不同观测降水数据优势,获取更为精细化降水产品,可以更好地再现区域降水时空异质性,这也使得多源降水数据精度评估、校正及融合应用成为当前研究热点。从降雨时空一致性、降雨量误差、日降雨事件探测能力方面对广西TRMM降雨产品进行了精度评估,并对比分析了TRMM对干旱的评估效果。结果表明:（1）广西TRMM降雨产品与气象站观测降雨存在显著的时空相关性,日尺度相关系数最低为0.5~0.6,月、年尺度相关系数则均在0.7以上;（2）广西TRMM降雨量总体略偏大,其相对偏差主要在0~10%;（3）TRMM产品在广西地区的日降雨事件探测能力优于全国平均水平,尤其在强降雨月份POD>0.75,但也存在降雨量较少的冬季月份准确率显著下降的现象。（4）TRMM降雨产品在广西干旱强度监测方面,能够揭示区域干旱的时空尺度效应和强度累积效应,具有比有限地面站点数据更精细化的空间异质性表达。相关成果有望促进区域多源降雨数据的校正及融合应用研究,为区域更有效的旱涝灾害评估、预报预警等提供科学支撑。      

Cut-off Lows in the South Africa region and their contribution to precipitation

The contribution of Cut-off Lows (CoLs) to precipitation and extreme rainfall frequency in South Africa has been quantified from 402 station records over the period 1979–2006. Firstly, 500 hPa CoL trajectories over Southern Africa and surrounding oceans were determined and their features thoroughly analyzed. In a second step, using daily precipitable water, outgoing long wave radiation data and station rainfall records, an area was defined where the occurrence of CoLs is associated with rainfall over South Africa. CoLs transiting in the 2.5°E–32.5°E/20°S–45°S are more likely to produce precipitation over the country. When 500 hPa CoLs are centered just off the west coast of the country (around 15°E/32.5°S) their impact is substantial in term of daily rainfall intensity and spatial coverage. CoL rainy days have been studied and it is shown that they significantly contribute to precipitation in South Africa, more strongly along the south and east coasts as well as inland, over the transition zone between the summer and winter rainfall domains where they contribute between 25 to more than 35 % of annual accumulation. At the country scale, CoL rainfall is more intense and widespread in spring than during other seasons. Over the analyzed period, a significant trend in annual CoLs’ frequency shows an increase of about 25 %. This increase is mainly realized in spring and in a lesser extent in summer. This trend is accompanied by a significant increase in the frequency of CoL rainy days specifically along the south coast and over the East of the country during the spring–summer period. In parallel, it is shown that from late spring until summer CoLs’ frequency varies significantly accordingly with large scale circulation modes of the Southern Hemisphere such as the Pacific South American pattern (PSA). This positive trend in CoLs’ frequency may be related with the positive trend in the PSA during the spring–summer period over the three last decades.     

基于时空不确定性的对流尺度集合预报效果评估检验

针对对流尺度天气系统的高度非线性特征和高分辨率模式预报结果存在时、空不确定性现象,以及当前邻域概率法主要考虑高分辨率预报结果的空间位移误差,而不能有效解决预报结果存在时间超前与滞后问题,将时间因素引入到邻域概率法中,结合一次强飑线过程进行对流尺度集合预报试验,并基于改进后的新型邻域概率法与分数技巧评分,对降水预报进行了不同时、空尺度的效果评估检验。结果表明:（1）邻域集合概率法和概率匹配平均法在极端降水的分数技巧评分远高于传统集合平均,弥补了集合平均对极端降水预报能力偏低的缺陷。（2）对于此类飑线过程的对流尺度天气系统而言,邻域半径为15—45 km的空间尺度能够改善降水位移误差的空间不确定性,并使其预报效果达到最优,其中15—30 km的邻域半径对于尺度更小的大量级降水事件预报能力更强。（3）对流尺度降水预报考虑时间尺度与降水强度存在着对应关系,不同时间尺度可以捕获到不同量级降水的时间不确定性。同时,时间尺度与空间尺度对于降水预报效果的影响是相互关联的。（4）改进的邻域概率法能够同时体现高分辨率模式预报结果在对流尺度降水事件上存在的时、空不确定性,实现了对流尺度降水在时、空尺度上的综合评估,并能为不同量级降水提供与其时、空尺度相匹配的概率预报结果。      

Spatial and temporal variability of summer rainfall over Ethiopia from observations and a regional climate model experiment

The spatial and temporal variability of rainfall over Ethiopia during the summer (JJAS) season is studied using observations (both station and satellite based) and model simulation data. The simulation dataset is generated using the fourth version of the International Center for Theoretical Physics Regional Climate Model (RegCM4) for the period 1989–2005. Ethiopia is first divided into 12 homogeneous regions using criteria including rotated empirical orthogonal function (REOF), spatial correlation, seasonal cycles, and topographical features. Spatially averaged observed and simulated rainfall time series are then generated and analyzed for each region. Standardized rainfall anomalies of the observations and the simulated data are highly correlated over the northern, western, northeastern, central, and southwestern regions, while a weak correlation is found over the border regions of the country. The dominant modes of rainfall variability are identified using REOF, while time–frequency variations of different dominant modes are described by wavelet analysis. The first leading patterns of rainfall and upper wind (averaged between 100 and 300 hPa) are highly correlated and exhibit similar features between simulation and observations over the northern, western, southwestern, and eastern regions of Ethiopia. The second loading pattern of rainfall and the first loading pattern of low-level wind (averaged between 850 and 1,000 hPa) exhibit a dipole structure across the southwestern and northeastern regions of the country. The dominant signals in the first rotated principal component (RPC) of rainfall and upper level wind fields show a period of 4–5 and 2–3 years, while the dominant signals in the second RPC show a period of 2–3 years at a 0.05 significance level. The correlations of significant RPCs across gauge, gridded, and model rainfall fields with that of low and upper level winds show the presence of a significant relationship (correlation exceeding ～0.6). Overall, the RegCM4 shows a good performance in simulating the spatial and temporal variability of precipitation over Ethiopia.     

Exploring spatiotemporal patterns and physical controls of soil moisture at various spatial scales

Soil moisture variability of various spatial scales is analyzed based on empirical orthogonal function (EOF) method using soil moisture datasets with various spatial resolutions: 1 km eco-hydrological model simulation, 0.25° passive microwave (Advanced Microwave Scanning Radiometer for the Earth Observing System, AMSR-E) dataset, and 0.5° land surface model simulation from Climate Predictor Center (CPC). All three datasets generate EOFs that explain similar variances with those generated from in situ observations from agro-meteorological network. Using AMSR-E product and eco-hydrological model simulation, it is found that the primary spatial pattern of soil moisture obtained from watershed scale has a strong connection to topographic attributes, followed by soil texture and rainfall variability, as suggested by the correlation between the primary EOF mode (EOF1) of soil moisture and landscape attributes. However, the EOF analysis of both AMSR-E and CPC datasets at regional scale reaches the conclusion that soil texture indices, such as sand and clay content, is of higher importance to soil moisture EOF1 spatial pattern (explaining 61 % variance) than topography is. Furthermore, correlation between soil moisture EOF1 and soil property is higher in spring than in autumn, which indicates that soil water-holding and drainage capabilities are more important under dry conditions. At national scale, the combined effects of topographic feature and soil property are clearly exhibited in EOF1. The study results reveal that different emphases should be placed on accurate acquisition of landscape attributes for soil moisture estimation according to various spatial scales.     

A study on the decreasing trend in tropical easterly jet stream (TEJ) and its impact on Indian summer monsoon rainfall

Using the NCEP/NCAR reanalysis wind and temperature data (1948–2011) and India Meteorological Department (IMD) rainfall data, a long-term trend in the tropical easterly jet stream and its effect on Indian summer monsoon rainfall has been explained in the present study. A decreasing trend in zonal wind speed at 100 mb (maximum decrease), 150 mb, and 200 mb (minimum) is observed. The upper-level (100, 150, and 200 mb) zonal wind speed has been correlated with the surface air temperature anomaly index (ATAI) in the month of May, which is taken as the difference in temperature anomaly over land (22.5°N–27.5°N, 80°E–90°E) and Ocean (5°S–0°S, 75°E–85°E). Significant high correlation is observed between May ATAI and tropical easterly jet stream (TEJ) which suggests that the decreasing land–sea temperature contrast could be one major reason behind the decreasing trend in TEJ. The analysis of spatial distribution of rainfall over India shows a decreasing trend in rainfall over Jammu and Kashmir, Arunachal Pradesh, central Indian region, and western coast of India. Increasing trend in rainfall is observed over south peninsular and northeastern part of India. From the spatial correlation analysis of zonal wind with gridded rainfall, it is observed that the correlation of rainfall is found to be high with the TEJ speed over the regions where the decreasing trend in rainfall is observed. Similarly, from the analysis of spatial correlation between rainfall and May ATAI, positive spatial correlation is observed between May ATAI and summer monsoon rainfall over the regions such as south peninsular India where the rainfall trend is positive, and negative correlation is observed over the places such as Jammu and Kashmir where negative rainfall trend is observed. The decreased land–sea temperature contrast in the pre-monsoon month could be one major reason behind the decreased trend in TEJ as well as the observed spatial variation in the summer monsoon rainfall trend. Thus, the study explained the long-term trend in TEJ and its relation with May month temperature over the Indian Ocean and land region and its effect on the trend and spatial distribution of Indian summer monsoon rainfall.     

Validation of TRMM Precipitation Radar satellite data over Indonesian region

Research has been conducted to validate monthly and seasonal rain rates derived from the Tropical Rainfall Measuring Mission Precipitation Radar (PR) using rain gauge data analysis from 2004 to 2008. The study area employed 20 gauges across Indonesia to monitor three Indonesian regional rainfall types. The relationship of PR and rain gauge data statistical analysis included the linear correlation coefficient, the mean bias error (MBE), and the root mean square error (RMSE). Data validation was conducted with point-by-point analysis and spatial average analysis. The general results of point-by-point analysis indicated satellite data values of medium correlation, while values of MBE and RMSE tended to indicate underestimations with high square errors. The spatial average analysis indicated the PR data values are lower than gauge values of monsoonal and semi-monsoonal type rainfall, while anti-monsoonal type rainfall was overestimated. The validation analysis showed very good correlation with the gauge data of monsoonal type rainfall, high correlation for anti-monsoonal type rainfall, but medium correlation for semi-monsoonal type rainfall. In general, the statistical error level of monthly seasonal monsoonal type conditions is more stable compared to other rainfall types. Unstable correlations were observed in months of high rainfall for semi-monsoonal and anti-monsoonal type rainfall.     

Streamflow trend analysis by considering autocorrelation structure,long-term persistence,and Hurst coefficient in a semi-arid region of Iran

Due to the substantial decrease of water resources as well as the increase in demand and climate change phenomenon, analyzing the trend of hydrological parameters is of paramount importance. In the present study, investigations were carried out to identify the trends in streamflow at 20 hydrometric stations and 11 rainfall gauging stations located in Karkheh River Basin (KRB), Iran, in monthly, seasonal, and annual time scales during the last 38 years from 1974 to 2011. This study has been conducted using two versions of Mann–Kendall tests, including (i) Mann–Kendall test by considering all the significant autocorrelation structure (MK3) and (ii) Mann–Kendall test by considering LTP and Hurst coefficient (MK4). The results indicate that the KRB streamflow trend (using both test versions) has decreased in all three time scales. There is a significant decreasing trend in 78 and 73 % of the monthly cases using the MK3 and MK4 tests, respectively, while these percentages changed to 80 and 70 % on seasonal and annual time scales, respectively. Investigation of the trend line slope using Theil–Sen’s estimator showed a negative trend in all three time scales. The use of MK4 test instead of the MK3 test has caused a decrease in the significance level of Mann–Kendall Z-statistic values. The results of the precipitation trends indicate both increasing and decreasing trends. Also, the correlation between the area average streamflow and precipitation shows a strong correlation in annual time scale in the KRB.     

尺度分解技术在定量降水临近预报检验中的应用

采用2004年Casati提出的强度-尺度检验技术,选取2008年汛期代表不同类型降水(对流云降水、层状云降水、混合云降水)的4个降水过程,从尺度分解角度入手,对"世界气象组织天气研究计划——北京奥运会预报示范项目"(WWRP B08FDP)项目中4个I临近预报参加系统(BJANC,GRAPES-SWIFT,STEPS,CARDS)的1h定量降水预报进行时空尺度分解检验,研究降水预报技巧与降水时空尺度和强度之间的关系。结果表明:尽管目前国际先进的临近预报系统的水平分辨率已高达1～2km,但其有技巧的临近预报能力主要集中于空间尺度大于32km、时间尺度大于1h的降水系统,而对小于这些尺度的降水系统预报能力仍非常有限;在不同时空尺度的临近预报降水误差中,60%以上的误差来自于空间尺度小于8km的降水,85%以上的误差来自于时间尺度小于1h的降水,传统的外推技术不能满足这些较小时空尺度降水预报的需求,要发展有效的预报方法来提高较小时空尺度降水的预报能力。将基于外推的临近预报和基于稠密观测资料、快速更新的数值预报的潜势预报相结合可能是一条有效的解决途径。     

概率密度匹配法对中国区域卫星降水资料的改进

为考察概率密度匹配法 (PDF方法) 对中国区域卫星反演降水产品系统误差订正的适用性,基于逐日和逐时我国地面观测降水量资料,引入PDF方法,分别对逐日0.25°×0.25°水平分辨率和逐时0.1°×0.1°水平分辨率的CMORPH (Climate Prediction Center Morphing Technique) 卫星降水产品的系统误差进行订正。在分析CMORPH卫星降水产品误差特征的基础上,根据两种资料不同的时空分辨率和误差特点,调整概率密度匹配时选取样本的时间和空间范围,设计相应的订正方案。评估结果表明: PDF方法订正后, 两种分辨率卫星降水资料在中国区域系统误差均显著减小,达到了理想的订正效果。在我国站点稀疏的西部地区,订正后的CMORPH卫星降水产品仍保持卫星观测的降水空间分布,降水量也明显接近于地面观测降水量。可见,PDF方法是中国区域卫星反演降水产品系统误差订正的一种有效方法。     

Simulations of precipitation using NRCM and comparisons with satellite observations and CAM: annual cycle

The accurate representation of rainfall in models of global climate has been a challenging task for climate modelers owing to its small space and time scales. Quantifying this variability is important for comparing simulations of atmospheric behavior with real time observations. In this regard, this paper compares both the statistical and dynamically forced aspects of precipitation variability simulated by the high-resolution (36?km) Nested Regional Climate Model (NRCM), with satellite observations from the Tropical Rainfall Measuring Mission (TRMM) 3B42 dataset and simulations from the Community Atmosphere Model (CAM) at T85 spatial resolution. Six years of rainfall rate data (2000?C2005) from within the Tropics (30°S?C30°N) have been used in the analysis and results are presented in terms of long-term mean rain rates, amplitude and phase of the annual cycle and seasonal mean maps of precipitation. Our primary focus is on characterizing the annual cycle of rainfall over four land regions of the Tropics namely, the Indian Monsoon, the Amazon, Tropical Africa and the North American monsoon. The lower tropospheric circulation patterns are analyzed in both the observations and the models to identify possible causes for biases in the simulated precipitation. The 6-year mean precipitation simulated by both models show substantial biases throughout the global Tropics with NRCM/CAM systematically underestimating/overestimating rainfall almost everywhere. The seasonal march of rainfall across the equator, following the motion of the sun, is clearly seen in the harmonic vector maps. The timing of peak rainfall (phase) produced by NRCM is in closer agreement with the observations compared to CAM. However like the long-time mean, the magnitude of seasonal mean rainfall is greatly underestimated by NRCM throughout the Tropical land mass. Some of these regional biases can be attributed to erroneous circulation and moisture surpluses/deficits in the lower troposphere in both models. Overall, the results seem to indicate that employing a higher spatial resolution (36?km) does not significantly improve simulation of precipitation. We speculate that a combination of several physics parameterizations and lack of model tuning gives rise to the observed differences between NRCM and the observations.