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.     

Evaluation of TRMM multi-satellite precipitation analysis (TMPA) against terrestrial measurement over a humid sub-tropical basin,India

To support the GPM mission which is homologous to its predecessor, the Tropical Rainfall Measuring Mission (TRMM), this study has been undertaken to evaluate the accuracy of Tropical Rainfall Measuring Mission multi-satellite precipitation analysis (TMPA) daily-accumulated precipitation products for 5 years (2008–2012) using the statistical methods and contingency table method. The analysis was performed on daily, monthly, seasonal and yearly basis. The TMPA precipitation estimates were also evaluated for each grid point i.e. 0.25° × 0.25° and for 18 rain gauge stations of the Betwa River basin, India. Results indicated that TMPA precipitation overestimates the daily and monthly precipitation in general, particularly for the middle sub-basin in the non-monsoon season. Furthermore, precision of TMPA precipitation estimates declines with the decrease of altitude at both grid and sub-basin scale. The study also revealed that TMPA precipitation estimates provide better accuracy in the upstream of the basin compared to downstream basin. Nevertheless, the detection capability of daily TMPA precipitation improves with increase in altitude for drizzle rain events. However, the detection capability decreases during non-monsoon and monsoon seasons when capturing moderate and heavy rain events, respectively. The veracity of TMPA precipitation estimates was improved during the rainy season than during the dry season at all scenarios investigated. The analyses suggest that there is a need for better precipitation estimation algorithm and extensive accuracy verification against terrestrial precipitation measurement to capture the different types of rain events more reliably over the sub-humid tropical regions of India.     

Accuracy validation of TRMM Multisatellite Precipitation Analysis daily precipitation products in the Lancang River Basin of China

Using statistical methods and contingency table method, this paper evaluates the accuracy of 12 years (1998–2009) Tropical Rainfall Measuring Mission Multisatellite Precipitation Analysis (TMPA) daily-accumulated precipitation products within a year, the dry season, and rain season for each of the five subbasins and for each grid point (0.25?×?0.25°) in the Lancang River basin by comparing the results with data from the 35 rain gauges. The results indicate that TMPA daily precipitation estimates tend to show an underestimation comparing to the rain gauge daily precipitations under any scenarios, especially for the middle stream in the dry season. The accuracy of TMPA-averaged precipitation deteriorates with the increase of elevation at both basin and grid scale, with upstream and downstream having the worst and best accuracy, respectively. A fair capability was shown when using daily TMPA accumulations to detect rain events at drizzle rain and this capability improves with the increase of elevation. However, the capability deteriorates when it is used to detect moderate rain and heavy rain events. The accuracy of TMPA precipitation estimate products is better in the rain season than in the dry season at all scenarios. Time difference and elevation are the main factors that have impact on the accuracy of TMPA daily-accumulated precipitation products.     

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.     

Estimation of Indian summer monsoon rainfall using Kalpana-1 VHRR data and its validation using rain gauge and GPCP data

In the present study, an attempt has been made to estimate and validate the daily and monthly rainfall during the Indian summer monsoon seasons of 2008 and 2009 using INSAT (Indian National Satellite System) Multispectral Rainfall Algorithm (IMSRA) technique utilizing Kalpana-1 very high resolution radiometer (VHRR) measurements. In contrary to infrared (IR), microwave (MW) rain rates are based on measurements that sense precipitation in clouds and do not rely merely on cloud top temperature. Geostationary satellites provide broad coverage and frequent refresh measurements but microwave measurements are accurate but sparse. IMSRA technique is the combination of the infrared and microwave measurements which make use of the best features of both IR- and MW-based rainfall estimates. The development of this algorithm included two major steps: (a) classification of rain-bearing clouds using proper cloud classification scheme utilizing Kalpana-1 IR and water vapor (WV) brightness temperatures (Tb) and (b) collocation of Kalpana-1 IR brightness temperature with Tropical Rainfall Measuring Mission (TRMM)-Precipitation Radar (PR) surface rain rate and establishment of a regression relation between them. In this paper, the capability of IMSRA as an operational algorithm has been tested for the two monsoon seasons 2008 and 2009. For this, IMSRA has been used to estimate daily and monthly rainfall and has been intercompared on daily and monthly scales with TRMM Multisatellite Precipitation Analysis (TMPA)-3B42 V6 product and Global Precipitation Climatology Project (GPCP) rain product during these two monsoon years. The daily and monthly IMSRA rainfall has also been validated against ground-based observations from Automatic Weather Station (AWS) Rain Gauge and Buoy data. The algorithm proved to be in good correlation with AWS data over land up to 0.70 for daily rain estimates except orographic regions like North-East and South-West India and 0.72 for monthly rain estimates. The validation with Buoys gives the reasonable correlation of 0.49 for daily rain estimates and 0.66 for monthly rain estimates over Tropical Indian Ocean.     

Towards combining GPM and MFG observations to monitor near real time heavy precipitation at fine scale over India and nearby oceanic regions

This is the first attempt to merge highly accurate precipitation estimates from Global Precipitation Measurement (GPM) with gap free satellite observations from Meteosat to develop a regional rainfall monitoring algorithm to estimate heavy rainfall over India and nearby oceanic regions. Rainfall signature is derived from Meteosat observations and is co-located against rainfall from GPM to establish a relationship between rainfall and signature for various rainy seasons. This relationship can be used to monitor rainfall over India and nearby oceanic regions. Performance of this technique was tested by applying it to monitor heavy precipitation over India. It is reported that our algorithm is able to detect heavy rainfall. It is also reported that present algorithm overestimates rainfall areal spread as compared to rain gauge based rainfall product. This deficiency may arise from various factors including uncertainty caused by use of different sensors from different platforms (difference in viewing geometry from MFG and GPM), poor relationship between warm rain (light rain) and IR brightness temperature, and weak characterization of orographic rain from IR signature. We validated hourly rainfall estimated from the present approach with independent observations from GPM. We also validated daily rainfall from this approach with rain gauge based product from India Meteorological Department (IMD). Present technique shows a Correlation Coefficient (CC) of 0.76, a bias of −2.72 mm, a Root Mean Square Error (RMSE) of 10.82 mm, Probability of Detection (POD) of 0.74, False Alarm Ratio (FAR) of 0.34 and a Skill score of 0.36 with daily rainfall from rain gauge based product of IMD at 0.25° resolution. However, FAR reduces to 0.24 for heavy rainfall events. Validation results with rain gauge observations reveal that present technique outperforms available satellite based rainfall estimates for monitoring heavy rainfall over Indian region.     

Evaluating the performance of remote sensing precipitation products CMORPH,PERSIANN, and TMPA,in the arid region of northwest China

The arid region of northwest China is a large area with complex topography. Hydrological research is limited by scarcity and uneven distribution of rain gauges. Satellite precipitation products provide wide coverage and high spatial–temporal resolutions, but the accuracy needs to be evaluated before application. In this paper, the reliability of four satellite precipitation products (CMORPH [Climate Prediction Center’s morphing technique], PERSIANN [Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks], TRMM [Tropical Rainfall Measuring Mission] 3B42, and TRMM 3B43) were evaluated through comparison with ground data or reported values on daily, monthly, and annual scales from 2003 to 2010. Indices including frequency bias index, probability of detection, and false alarm ratio were used to evaluate recorded precipitation occurrences; relative mean bias, the correlation coefficient, and the Nash coefficient were used to assess precipitation amount. Satellite precipitation products were more accurate in the warm than in the cold season, and performed better in northern Xinjiang than in other regions during the cold season. CMORPH and PERSIANN tended to overestimate precipitation. TRMM 3B42 and TRMM 3B43 performed best because the former most accurately detected precipitation occurrences on a daily scale, and both produced accurate space–time distribution of precipitation and the best consistency with rain gauge observations. Only a few monthly precipitation values for TRMM 3B42 and TRMM 3B43, and annual precipitation values for TRMM 3B42 were with satisfactory precision. TRMM3B42 and TRMM 3B43 are therefore recommended, but correction will be needed before application. Factors including elevation, relative relief, longitude, and latitude had significant effects on the performance of satellite precipitation products, and these factors may be helpful in correcting satellite precipitation.     

Validation of Daily Precipitation from Two High-Resolution Satellite Precipitation Datasets over the Tibetan Plateau and the Regions to Its East

Daily precipitation amounts and frequencies from the CMORPH (Climate Prediction Center Morphing Technique) and TRMM (Tropical Rainfall Measuring Mission) 3B42 precipitation products are validated against warm season in-situ precipitation observations from 2003 to 2008 over the Tibetan Plateau and the regions to its east. The results indicate that these two satellite datasets can better detect daily precipitation frequency than daily precipitation amount. The ability of CMORPH and TRMM 3B42 to accurately detect daily precipitation amount is dependent on the underlying terrain. Both datasets are more reliable over the relatively flat terrain of the northeastern Tibetan Plateau, the Sichuan basin, and the mid-lower reaches of the Yangtze River than over the complex terrain of the Tibetan Plateau. Both satellite products are able to detect the occurrence of daily rainfall events; however, their performance is worse in regions of complex topography, such as the Tibetan Plateau. Regional distributions of precipitation amount by precipitation intensity based on TRMM 3B42 are close to those based on rain gauge data. By contrast, similar distributions based on CMORPH differ substantially. CMORPH overestimates the amount of rain associated with the most intense precipitation events over the mid-lower reaches of the Yangtze River while underestimating the amount of rain associated with lighter precipitation events. CMORPH underestimates the amount of intense precipitation and overestimates the amount of lighter precipitation over the other analyzed regions. TRMM 3B42 underestimates the frequency of light precipitation over the Sichuan basin and the mid-lower reaches of the Yangtze River. CMORPH overestimates the frequencies of weak and intense precipitation over the mid-lower reaches of the Yangtze River, and underestimates the frequencies of moderate and heavy precipitation. CMORPH also overestimates the frequency of light precipitation and underestimates the frequency of intense precipitation over the other three regions. The TRMM 3B42 product provides better characterizations of the regional gamma distributions of daily precipitation amount than the CMORPH product, for which the cumulative distribution functions are biased toward lighter precipitation events.     

Evaluation of TMPA satellite-based research and real-time rainfall estimates during six tropical-related heavy rainfall events over Louisiana, USA

This study focuses on the evaluation of 3-hourly 0.25° × 0.25° satellite-based rainfall estimates produced by the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA). The evaluation is performed during six heavy rainfall events that were generated by tropical storms passing over Louisiana, United States. Two surface-based rainfall datasets from gauge and radar observations are used as a ground reference for evaluating the real-time (RT) version of the TMPA product and the post-real-time bias adjusted research version. The evaluation analysis is performed at the native temporal and spatial scales of the TMPA products, 3-hourly and 0.25° × 0.25°. Several graphical and statistical techniques are applied to characterize the deviation of the TMPA estimates from the reference datasets. Both versions of the TMPA products track reasonably well the temporal evolution and fluctuations of surface rainfall during the analyzed storms with moderate to high correlation values of 0.5–0.8. The TMPA estimates reported reasonable levels of rainfall detection especially when light rainfall rates are excluded. On a storm scale, the TMPA products are characterized by varying degrees of bias which was mostly within ± 25% and ± 50% for the research and RT products, respectively. Analysis of the error distribution indicated that, on average, the TMPA products tend to overestimate small rain rates and underestimate large rain rates. Compared to the real-time estimates, the research product shows significant improvement in the overall and conditional bias, and in the correlation coefficients, with slight deterioration in the probability of detecting rainfall occurrences. A fair agreement in terms of reproducing the tail of the distribution of rain rates (i.e., probability of surface rainfall exceeding certain thresholds) was observed especially for the RT estimates. Despite the apparent differences with surface rainfall estimates, the results reported in this study highlight the TMPA potential as a valuable resource of high-resolution rainfall information over many areas in the world that lack capabilities for monitoring landfalling tropical storms.     

COMPARISON OF PERSIANN AND TMPA DAILY PRECIPITATION ESTIMATES OVER HUNAN PROVINCE OF CHINA

This study presented a detailed comparison of daily precipitation estimates from Precipitation Estimation from Remote Sensing Information using Artificial Neural Network (PERSIANN) and Tropical Rainfall Measuring Mission (TRMM) Multi -satellite Precipitation Analysis (TMPA) over Hunan province of China from 1998 to 2014. The ground gauge observations are taken as the reference. It is found that overall TMPA clearly outperforms PERSIANN, indicating by better statistical metrics (including correlation coefficient, root mean square error and relative bias). For the geospatial pattern, although both products are able to capture the major precipitation features (e.g., precipitation geospatial homogeneity) in Hunan, yet PERSIANN largely underestimates the precipitation intensity throughout all seasons. In contrast, there is no clear bias tendency from TMPA estimates. Precipitation intensity analysis showed that both the occurrence and amount histograms from TMPA are closer to the gauge observations from spring to autumn. However, in the winter season PERSIANN is closer to gauge observation, which is likely due to the ground contamination from the passive microwave sensors used by TMPA.     

基于TRMM卫星的近10a甘肃临夏降水变化特征

基于2001~2010年TRMM 3B43降水资料和数字高程模型（DEM）数据,采用回归模型＋残差的方法,对甘肃临夏回族自治州近10 a的TRMM 3B43降水数据进行降尺度运算,并结合研究区6个雨量站的观测值,对TRMM 3B43降尺度结果进行精度检验,在此基础上定量研究了临夏回族自治州近10 a的降水量时空变化特征。结果表明：TRMM 3B43降尺度降水量数据整体上具有一定的可信度,但比地面台站的观测值偏小;甘肃临夏州年降水量呈现出由西南向东北递减的趋势,且降水量随着海拔高度的升高而逐渐增加,两者相关系数为0.82;年内降水主要集中在5~9月,基本占全年降水量的70%以上,其中6月降水量最大,12月降水量最小。     

Daily precipitation forecast of ECMWF verified over Iran

In this paper, the performance of the Centre for Medium Range Weather Forecast (ECMWF) model (t?+?27 h to t?+?51 h) in predicting precipitation is discussed. This model is the first, which has been verified over Iran. The spatial resolution of the model is 0.351° and the 24-h forecasts are compared with daily observations. The study concentrates on year 2001 and the precipitation measurements were collected from the data of 2,048 rain gauges in Iran. The accuracy of four different interpolation methods (nearest neighborhood, inverse distance, kriging, and upscaling) was investigated. Using cross-validation, the inverse distance method (IDM) with minimum mean error was applied. Verification results are given in terms of difference fields (mean error?=?0.46 mm/day), rank–order correlation coefficients (0.70), as well as accuracy scores (false alarm ratio?=?0.50 and probability of detection?=?0.60) and skill scores (true skill statistics [TSS]?=?0.45) in year 2001. The position of the rain band was only partly captured by the ECMWF model; however, the position of maximum precipitations agrees with the observations well. The results show that the high values of TSS are associated with the large amounts of precipitation (over 25 mm). Slight to moderate precipitation events have been underforecasted by the model (bias?<?1) and it leads to a small value of TSS for these thresholds (5–25 mm/day). The ECMWF model has better performance in high and mountainous regions than over flat terrain and in deserts. Comparing TSS over the Alborz and the Zagros Mountains, it is obvious that the ability of the model to predict the convective precipitation events needs some improvement. The amount of daily precipitation has been also slightly overestimated over Iran. From the beginning of January up to 21 March 2001, the ECMWF time series indicates an obvious phase shift of 1 day, although in other months, no phase shift is noticed.     

Comparison of TRMM and water district rain rates over New Mexico

This paper compares monthly and seasonal rain rates derived from the Version 5 (V5) and Version 6 (V6) TRMM Precipitation Radar (TPR, TSDIS reference 2A25), TRMM Microwave Imager (TMI, 2A12), TRMM Combined Instrument (TCI, 2B31), TRMM calibrated IR rain estimates (3B42) and TRMM merged gauge and satellite analysis (3B43) algorithms over New Mexico (NM) with rain gauge analyses provided by the New Mexico water districts (WD). The average rain rates over the NM region for 1998–2002 are 0.91mmd?1 for WD and 0.75, 1.38, 1.49, 1.27, and 1.07mmd?1 for V5 3B43, 3B42, TMI, PR and TCA; and 0.74, 1.38, 0.87 and 0.97 mm d?1 for V6 3B43, TMI, TPR and TCA, respectively. Comparison of V5 3B43 with WD rain rates and the daily TRMM mission index (TPR and TMI) suggests that the low bias of V5 3B43 for the wet months (summer to early fall) may be due to the non-inclusion of some rain events in the operational gauge analyses that are used in the production of V5 3B43. Correlation analyses show that the WD rain rates vary in phase, with higher correlation between neighboring WDs. High temporal correlations (>0.8) exist between WD and the combined algorithms (3B42, 3B43 and TCA for both V5 and V6) while satellite instrument algorithms (PR, TMI and TCI) are correlated best among themselves at the monthly scale. Paired t-tests of the monthly time series show that V5 3B42 and TMI are statistically different from the WD rain rates while no significant difference exists between WD and the other products. The agreements between the TRMM satellite and WD gauge estimates are best for the spring and fall and worst for winter and summer. The reduction in V6 TMI (?7.4%) and TPR (?31%) rain rates (compared to V5) results in better agreement between WD estimates and TMI in winter and TPR during summer.     

中国区域逐日融合降水数据集与国际降水产品的对比评估

中国国家气象信息中心基于2400多个国家级台站观测日降水量和CMORPH卫星反演降水产品,采用概率密度匹配和最优插值相结合的两步数据融合方法,研制了中国区域1998年以来的0.25°×0.25°分辨率的逐日融合降水产品(CMPA_Daily)。通过该数据集与广泛应用于中国天气气候领域的两种国际上降水融合产品TRMM 3B42(Tropical Rainfall Measuring Mission, 3B42)和GPCP(Global Precipitation Climatology Project, 1 degree daily)的对比评估,考察CMPA_Daily产品的质量,评价其能否合理体现中国降水的天气气候特征。首先利用2008—2010年5—9月独立检验数据定量对比了CMPA_Daily、TRMM 3B42和GPCP 三种降水产品的误差,结果表明,在误差的时间变化和空间分布上,CMPA_Daily均具有最高的相关系数和最小的平均偏差及均方根误差,TRMM 3B42其次,GPCP的误差相对较大。CMPA_Daily只低估了大暴雨,TRMM 3B42低估了大雨以上量级的降水,而GPCP低估了除小雨以外的所有降水。CMPA_Daily产品因融入了更多的站点观测信息,不论在中国东部沿海,还是中西部地形复杂区,其精度均优于TRMM 3B42和GPCP产品,即使在站点稀疏的青藏高原地区,CMPA_Daily降水量也更加接近站点观测,呈现明显的高相关。CMPA_Daily与独立检验数据的高相关在地形起伏时效果也较稳定,TRMM和GPCP的相关系数则随着地形变化幅度陡变而非常明显地降低。进一步通过对比分析各降水产品1998—2012年的气候平均降水特征表明,3种资料对中国区域气候平均降水量、降水强度、频率分布以及年际变化的总体描述基本一致,因有效融入了更多的中国站点观测信息,不论降水空间分布还是降水量,CMPA_Daily与地面观测均最为接近,在中国的中东部大部分地区对降水的估计精度明显更高,而在站点分布较稀疏的青藏高原地区,CMPA_Daily的降水分布型与TRMM、GPCP卫星融合资料类似,较地面站点插值产品更能体现出合理的降水分布。对中国强降水事件监测对比表明,CMPA_Daily产品可以更加准确地描述降水的强度变化,细致刻画降水空间分布,在把握降水小尺度特征上具有明显的优势,体现出高分辨率、高精度降水产品的特点。     

利用雷达资料对自动雨量计实时质量控制的方法研究

自动雨量计资料是对降水的直接测量,在流域面雨量计算、气候研究、气象服务等方面具有重要意义。但是,由于风力、蒸发、灌溉、校准、漏斗堵塞、机械故障、信号传输等原因往往造成其存在不同类型的系统误差和随机误差, 自动雨量计数据在定量使用前需要进行质量控制。目前,天气雷达以其高时空分辨率的优势已经成为监测降水的重要手段,本文首先采用两步校准法改善雷达估测降水,然后对雷达—雨量计对之间的差异进行统计学的分析,确定自动雨量计质量控制的一些标准,从而对雨量计进行质量控制。最后用两个降水过程对自动雨量计质量控制的结果进行了检验,结果表明:两步校准法改善了雷达估测降水的系统性偏差,并减小了雨量计站点上的相对误差;可以利用雷达估测降水实现对自动雨量计的实时质量控制,就整个数据集而言,约0.1%的数据被怀疑为误判,误判的自动雨量计主要位于雨带的边缘。但该质量控制算法同时也存在一定的局限性:在雨带的边缘或没有天气雷达覆盖的区域,以及雷达资料存在数据质量问题的情况下,往往会造成对雨量计的误判。     

华东中尺度地形对浙北暴雨影响的模拟研究

以一次梅雨降水为例，利用中尺度模式进行一系列中尺度地形对降水的增幅影响的敏感性试验。结果表明，中尺度地形对强降水区域的分布和强度有很大的影响，强降水中心位于地形附近，地形引起的１２小时降水增幅高达总降水的９０％以上；中尺度地形作为一种外界迫动，初始在低层形成气旋性辐合和水汽热量的集中，然后通过凝结潜热释放所造成的中高层增温和高层辐散，使得地形垂直环流加强和向上伸展。于是在降水、潜热释放与地形垂直环流之间出现一种正反馈机制，导致地形对降水的强烈增幅；同时午后下垫面加热所形成的不稳定层结也有利于地形垂直环流的不稳定发展，产生新的雨峰；初始场的中尺度扰动似乎在降水的地形性增幅中并不起明显作用。     

Downscaling and projection of precipitation from general circulation model predictors in an equatorial climate region by the automated regression-based statistical method

The authors have applied an automated regression-based statistical method, namely, the automated statistical downscaling (ASD) model, to downscale and project the precipitation climatology in an equatorial climate region (Peninsular Malaysia). Five precipitation indices are, principally, downscaled and projected: mean monthly values of precipitation (Mean), standard deviation (STD), 90th percentile of rain day amount, percentage of wet days (Wet-day), and maximum number of consecutive dry days (CDD). The predictors, National Centers for Environmental Prediction (NCEP) products, are taken from the daily series reanalysis data, while the global climate model (GCM) outputs are from the Hadley Centre Coupled Model, version 3 (HadCM3) in A2/B2 emission scenarios and Third-Generation Coupled Global Climate Model (CGCM3) in A2 emission scenario. Meanwhile, the predictand data are taken from the arithmetically averaged rain gauge information and used as a baseline data for the evaluation. The results reveal, from the calibration and validation periods spanning a period of 40 years (1961–2000), the ASD model is capable to downscale the precipitation with reasonable accuracy. Overall, during the validation period, the model simulations with the NCEP predictors produce mean monthly precipitation of 6.18–6.20 mm/day (root mean squared error 0.78 and 0.82 mm/day), interpolated, respectively, on HadCM3 and CGCM3 grids, in contrast to 6.00 mm/day as observation. Nevertheless, the model suffers to perform reasonably well at the time of extreme precipitation and summer time, more specifically to generate the CDD and STD indices. The future projections of precipitation (2011–2099) exhibit that there would be an increase in the precipitation amount and frequency in most of the months. Taking the 1961–2000 timeline as the base period, overall, the annual mean precipitation would indicate a surplus projection by nearly 14~18 % under both GCM output cases (HadCM3 A2/B2 scenarios and CGCM3 A2 scenario). According to the model simulation, the September–November periods might be the more significant months projecting the increment of the precipitation amount around over 50 %, while the precipitation deficit would be seen in March–May periods.     

0907号热带风暴“天鹅”暴雨的数值模拟

利用新一代中尺度数值模式WRF,对登陆后滞留粤西地区近50 h并带来了暴雨以上强降水的0907号热带风暴"天鹅"进行数值模拟,取得了较好的效果。该模式成功模拟出"天鹅"的移动路径、强度和强降水分布,暴雨的中心强度与实况基本一致。利用模式输出的高分辨率结果分析研究"天鹅"的暴雨降水原因,通过改变地形高度的敏感性试验表明,沿海地区地形的抬升作用对降雨有显著的增幅作用,并且使降水分布更加不均匀。     

Evaluation of a climate simulation in Europe based on the WRF–NOAH model system: precipitation in Germany

The Weather Research and Forecast (WRF) model with its land surface model NOAH was set up and applied as regional climate model over Europe. It was forced with the latest ERA-interim reanalysis data from 1989 to 2008 and operated with 0.33° and 0.11° resolution. This study focuses on the verification of monthly and seasonal mean precipitation over Germany, where a high quality precipitation dataset of the German Weather Service is available. In particular, the precipitation is studied in the orographic terrain of southwestern Germany and the dry lowlands of northeastern Germany. In both regions precipitation data is very important for end users such as hydrologists and farmers. Both WRF simulations show a systematic positive precipitation bias not apparent in ERA-interim and an overestimation of wet day frequency. The downscaling experiment improved the annual cycle of the precipitation intensity, which is underestimated by ERA-interim. Normalized Taylor diagrams, i.e., those discarding the systematic bias by normalizing the quantities, demonstrate that downscaling with WRF provides a better spatial distribution than the ERA interim precipitation analyses in southwestern Germany and most of the whole of Germany but degrades the results for northeastern Germany. At the applied model resolution of 0.11°, WRF shows typical systematic errors of RCMs in orographic terrain such as the windward–lee effect. A convection permitting case study set up for summer 2007 improved the precipitation simulations with respect to the location of precipitation maxima in the mountainous regions and the spatial correlation of precipitation. This result indicates the high value of regional climate simulations on the convection-permitting scale.     

基于漂移克里金融合雷达、雨量计定量估测降水研究

文中介绍了一种新的融合雷达和雨量计数据开展定量估测降水研究的空间信息统计学方法—Kriging with external drift(KED)方法。该方法能很好地融合高精度、低时空分辨率的雨量计数据和低精度、高时空分辨率的雷达数据进行插值。通过变异函数描述降水场的空间结构信息,能够充分利用数据间的空间相关性,来改进估测精度和提高处理速度。利用其优良的数学特性,以期在定量估测降水业务研究上进行新的探索和尝试。选用湖南省有代表意义的3次降水过程资料,通过雷达直接估测降水（RAD）、变分校准（VAR）以及KED 3种方法,分别与雨量计测量值进行对比分析,选用代表站进行交叉验证结果均表明：RAD的均方差、绝对误差、相对误差最大,VAR次之,而KED最小。KED估测的结果与雨量计测量降水最为接近,估测效果最好;3种方法与雨量计实测值计算一定范围的误差频率,KED估测值具有最小的均方差和最小的标准差,且误差分布相对集中在0值附近,斜度和峰度最佳,试验证明该方法不仅能提高降水估测精度,且优于其他方法,VAR均方差次之,RAD均方差效果相对较差。联合雷达、雨量计估测降水的实质是把雷达估测值与雨量计测量的结果相融合,以雨量计来校准雷达估测值,保留了雷达探测到降水的中、小尺度精细特征。校准后的雨量场数值接近雨量计测值,而且能够准确反映雷达测得的降水分布形式。