TRMM 卫星降水数据在商洛地区的精度分析及订正

利用商洛地区193 个国家和区域气象站点实测降水数据，从时间和空间尺度，运用相关分析、偏差、均方根误差等统计分析方法对 TRMM 卫星 3B43 v7降水数据在商洛地区的适用性进行了分析，在此基础上采用比值系数法对TRMM 卫星月降水产品进行订正。结果表明：①在时间尺度上，TRMM 月降水数据与站点实测月降水数据线性高度相关，TRMM 降水数据低估站点实测降水数据。②对月、季和年尺度的TRMM和站点降水量进行比较，决定系数R２均在0.8以上，拟合优度较高，季降水量结果拟合结果较月降水量有所提高。③利用比值系数法对TRMM降水量产品进行偏差订正，验证结果表明，订正后的TRMM 3B43月降水结果偏差更小，决定系数更高。TRMM 降水精度与降水强度和地形有关，TRMM月降水量与实测月降水量时间序列趋势一致，拟合性较高，分布规律相一致。总体上，降水量多的年份，TRMM 数据倾向于低估降水量，反之，高估站点降水量。通过对TRMM 卫星降水产品在商洛地区的精度评估和订正，为该地区地面降水数据提供有效补充。     

四川区域融合降水产品的质量评估

本文利用四川省156个国家地面气象观测自动站2018年逐小时降水资料,从降水产品与观测值的对比、降水产品误差空间特征、降水产品误差月变化、不同降水量级的误差特征等方面,对国家气象信息中心研制的中国区域1h、0.05° × 0.05°分辨率的地面-卫星-雷达三源融合实时降水产品和地面-卫星二源融合快速降水产品在四川区域的适用性进行对比评估。研究结果表明,两套融合降水产品能较好的反映四川区域年内小时降水的时空变化特征,与站点观测降水相比,两套融合降水产品均存在一定程度的低估,且随着降水量级的增大,均方根误差值也相应增大。两套融合降水产品相比,融合了雷达资料的三源融合降水产品各项指标均优于二源融合降水产品,数据质量更高。      

怒江上游TRMM 3B42V7降水产品资料时空验证及降水特征分析

利用1998-2013年热带测雨卫星TRMM 3B42V7降水数据产品和18个气象站点观测数据对怒江上游进行了降雨时空分布特征的对比分析,研究了TRMM 3B42V7数据产品在该区域的精度。结果显示:研究区内TRMM 3B42V7数据和站点数据相关性月尺度最强(R0.9),年尺度次之(R0.5),日尺度较差(R0.5);流域面雨量两组数据在月尺度上有极强的相关性(R≈0.98),时间序列拟合较好,但在降水量大的月份TRMM 3B42V7数据较站点观测数据略偏大;不同时间尺度下两组数据的空间分布特征总体具有较好的一致性,局部分布特征有差异;流域西北和东南局部区域TRMM降水分别有低估和高估趋势,其他大部区域相当。利用TRMM 3B42V7数据对流域降水进行季节占比分析,结果显示,流域降水季节分配不均匀,夏季(6-8月)降水占全年降水总量的比例较大,高达42%~72%;其他春(3-5月)、秋(9-11月)、冬(12月至次年2月)三季节降水整体较少,总占比为28%~58%。     

CMORPH和TRMM 3B42降水估计产品的评估检验

文章利用2007-2010年中国2447个站点的逐小时降水观测数据对同期CMORPH和TRMM 3B42卫星降水估计产品进行了检验和评估。经过对比分析得出:CMORPH和TRMM 3B42卫星降水资料与地面台站资料的日平均降水量空间分布具有较好的相似性,3 h降水量的相关系数在大部分地区分别在0.5和0.4以上;偏差均在±0.25 mm之间,但CMORPH存在显著的南北差异;平均绝对误差、相对误差和均方根误差均存在明显的季节周期性变化;两种卫星资料能够较好反映我国大部分区域夏季降水日变化特征,但在部分地区存在大的偏差;CMORPH和TRMM 3B42总空演率分别为7.23%和2.63%,总漏演率分别为3.25%和5.50%。     

基于GWR模型的贵州喀斯特山区TRMM 3B43降水资料降尺度分析

基于降水与地形起伏之间的非平稳关系,结合有限的观测降水数据,利用GWR回归模型,对贵州喀斯特山区的TRMM 3B43降水资料进行降尺度和校准,最终得到空间分辨率为1 km×1 km的降水量分布数据并进行了验证。结果显示:(1)考虑地形起伏和降水空间非平稳性的GWR模型,提高了贵州喀斯特山区TRMM 3B43遥感降水资料的空间分辨率和准确度。(2)不同时间尺度的验证结果表明,在与观测降水的相关统计中,TRMM降尺度降水具有较TRMM 3B43降水更高的统计精度和更小的误差,更接近于地面观测降水;该降尺度算法在贵州降水较少的时间尺度更加接近真实值。(3)当TRMM 3B43可以被重采样的地形起伏度(RDLS)进行准确预测时,TRMM 3B43的精度是GWR降尺度算法中的主要误差源;当区域的降水与地形起伏弱相关或无关时,应考虑引入其他影响降水的空间变量来修正这一空间非平稳性关系。     

基于江西省水文资料对中国融合降水产品的质量评估

利用江西省2015年4月至2016年3月水文站观测降水数据,在小时尺度上,对中国国家气象信息中心研制的5和10 km融合降水产品进行质量评估,同时与美国国家海洋大气局(NOAA)气候预测中心卫星反演降水产品(CMORPH)、中国国家气象信息中心研制的东亚区域多卫星集成降水产品(EMSIP)两套卫星降水产品进行对比评估。研究分析各类降水产品的数据误差及其时空变化规律,验证融合降水产品在特征区域的适用性。研究结果表明:融合降水和卫星降水均能较好地反映年内小时降水的变化趋势,与水文站观测降水相比,四套降水资料均存在一定程度低估,其中卫星降水产品低估较大。融合降水产品的数据质量较高,其中5 km融合降水产品的数据精度(R=0.81,RMSE=2.12 mm·h~(-1),RE=-5.4%)基本优于10 km融合降水产品(R=0.78,RMSE=2.3 mm·h~(-1),RE=-5.1%),卫星降水产品与水文站观测降水存在较大的偏差,CMORPH和EMSIP的相关系数分别仅为0.19和0.24。各降水产品误差具有相同的月变化趋势,融合降水产品的误差变化幅度明显要小于卫星降水产品。四套降水产品的相关性随着降水量级增大而增加,融合降水产品能够准确反映降水的空间结构和中心位置,5 km融合降水产品对强降水的监测能力更具有优势。     

三源融合降水产品在陕西省的适用性评估

基于陕西省391个自动站逐小时降水量观测数据对国家级格点实况三源融合降水产品的适用性进行检验评估,结果表明:融合降水产品与站点观测之间的误差小、相关性高,但融合降水产品的标准差和极大值明显小于站点观测;相关系数较低的站点以区域站为主,国家气象观测站的效果明显优于区域站;误差时空分布和降水特征关系密切,在降水频次增多和强度增大时,融合降水产品相比站点观测的误差增大。将融合降水产品视为一种“预报”,站点观测资料作为“真值”进行分级检验,结果显示:融合降水产品可以较好反映有无降水,随降水量级增大空报率变化平稳,漏报率增长明显,导致TS评分逐渐下降。对典型个例的误差成因分析显示:融合降水产品可以较好地体现降水起止时间及性质、强弱演变趋势,但对雨强较大的区域性降水、分散性局地强降水表现欠佳。多种指标综合显示:融合降水产品小量级降水准确率高,对大雨以上量级降水强度有一定程度削弱;陕南秦巴山地的融合降水产品与站点观测偏差较大,应用中需特别关注。     

我国高分辨率降水融合资料的适用性评估

利用国家气象信息中心研制的全国30000多个地面自动站降水与 CMORPH (Climate Prediction Center Morphing technique)卫星反演降水融合而成的融合降水产品,分析了融合降水平均偏差和均方根误差的时空分布特征,探讨了不同降水量级以及站点稀疏区和密集区的融合效果,结果表明：融合降水的平均偏差和均方根误差量值均较卫星反演降水有显著减小,随时间的变化幅度不大且误差的区域性差异减弱;融合降水不同量级降水日数分布接近于地面观测降水,虽高估了雨强小于等于4 mm/d的降水,低估了大于4 mm/d高值降水,但同一量级下的误差比卫星反演降水大幅减小,且随着降水强度的增加改善效果明显;站点密集区的融合降水值主要是取决于地面观测降水;站点稀疏区在没有站点分布时,融合降水值主要取决于卫星反演降水,但随着站点个数增加,地面观测降水在融合降水中所占比重逐渐增大,且超过了卫星反演降水的作用。可见融合降水充分有效利用了地面观测降水和卫星反演降水各自的优势,融合效果明显。     

卫星反演降水资料在青藏高原地区的适用性分析

利用2000—2012年青藏高原附近地区251个台站的降水观测资料评估了CMOR.PH、PERSI—ANN、TRMM3B41RT、TRMM3B42RT和TRMM3B42V7等5种卫星反演降水资料在青藏高原地区的差异性和一致性。结果表明,5种卫星反演降水资料均能较好地表征降水量在青藏高原地区从东南向西北递减和夏季降水多、冬季降水少的特征。通过分析相对误差和空间相关系数表明,5种卫星资料在夏季的反演效果最好、冬季最差,白天好于夜间。相对于其他4种资料,TRMM3B42V7资料与观测值之间的差异最小,除了冬季一段较短时间内空间相关系数较低外,一年之中大部分时段空间相关系数都在0.5以上。CMORPH仅次于TRMM3B42V7,在青藏高原地区的适用性也较好;对不同等级降水频数的反演效果表明,CMORPH和TRMM3B42V7反演的小雨降水频数与台站观测值基本一致,高估了大雨和暴雨的降水频数,而TRMM3B42V7对中雨降水频数的反演较为合理。     

CMPA降水资料在中国地区不同地形下的精度评价研究

以2001—2010年中国地面自动站降水资料为基准,对中国大陆范围内CMPA(CMPA_Daily)降水资料进行精度评价研究,并与CMORPH1.0(CPC MORPHing technique gauge-satellite)、TRMM3B43V7(Tropical Rainfall Measuring Mission 3B43)降水资料精度进行对比,并进一步结合高程、坡度、坡向、坡向修正因子分析地形因子对数据质量的影响并探讨不同地貌类型下数据的精度。结果显示:CMPA在年、月尺度上均能较好地反映降水的多寡,与站点实测数据具有较高的相关性,误差波动较为平稳,数据质量及稳定性优于CMORPH与TRMM;从时间序列曲线显示CMPA的精度呈现较为明显的季节性差异,均方根误差夏季高于冬季,相关系数、百分比偏差、平均相对误差冬季高于夏季,总体而言CMPA夏季的误差高于冬季,由于夏季降水的基数大而导致了百分比偏差以及平均相对误差较低;分析地形的影响表明,高程、坡度对数据质量的影响大于坡向与坡向修正因子;在复杂地形下,高海拔与高坡度地区CMPA精度均有所降低,但降水资料的精度仍然优于CMORPH与TRMM。     

Tropical Precipitation Estimated by GPCP and TRMM PR Observations

In this study, tropical monthly mean precipitation estimated by the latest Global Precipitation Climatology Project （GPCP） version 2 dataset and Tropical Rainfall Measurement Mission Precipitation Radar （TRMM PR） are compared in temporal and spatial scales in order to comprehend tropical rainfall climatologically. Reasons for the rainfall differences derived from both datasets are discussed. Results show that GPCP and TRMM PR datasets present similar distribution patterns over the Tropics but with some differences in amplitude and location. Generally, the average difference over the ocean of about 0.5 mm d^-1 is larger than that of about 0.1 mm d^-1 over land. Results also show that GPCP tends to underestimate the monthly precipitation over the land region with sparse rain gauges in contrast to regions with a higher density of rain gauge stations. A Probability Distribution Function （PDF） analysis indicates that the GPCP rain rate at its maximum PDF is generally consistent with the TRMM PR rain rate as the latter is less than 8 mm d^-1. When the TRMM PR rain rate is greater than 8 mm d^-1, the GPCP rain rate at its maximum PDF is less by at least 1 mm d^-1 compared to TRMM PR estimates. Results also show an absolute bias of less than 1 mm d^-1 between the two datasets when the rain rate is less than 10 mm d^-1. A large relative bias of the two datasets occurs at weak and heavy rain rates.     

A strategy for merging objective estimates of global daily precipitation from gauge observations,satellite estimates,and numerical predictions

This paper describes a strategy for merging daily precipitation information from gauge observations, satellite estimates(SEs), and numerical predictions at the global scale. The strategy is designed to remove systemic bias and random error from each individual daily precipitation source to produce a better gridded global daily precipitation product through three steps.First, a cumulative distribution function matching procedure is performed to remove systemic bias over gauge-located land areas. Then, the overall biases in SEs and model predictions(MPs) over ocean areas are corrected using a rescaled strategy based on monthly precipitation. Third, an optimal interpolation(OI)–based merging scheme(referred as the HL-OI scheme)is used to combine unbiased gauge observations, SEs, and MPs to reduce random error from each source and to produce a gauge—satellite–model merged daily precipitation analysis, called BMEP-d(Beijing Climate Center Merged Estimation of Precipitation with daily resolution), with complete global coverage. The BMEP-d data from a four-year period(2011–14) demonstrate the ability of the merging strategy to provide global daily precipitation of substantially improved quality.Benefiting from the advantages of the HL-OI scheme for quantitative error estimates, the better source data can obtain more weights during the merging processes. The BMEP-d data exhibit higher consistency with satellite and gauge source data at middle and low latitudes, and with model source data at high latitudes. Overall, independent validations against GPCP-1DD(GPCP one-degree daily) show that the consistencies between BMEP-d and GPCP-1DD are higher than those of each source dataset in terms of spatial pattern, temporal variability, probability distribution, and statistical precipitation events.     

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.     

一套新的30年全球台站逐日降水资料集:质量控制和评估

对来自全球无线通讯系统(GTS) 1980-2009年全球台站逐日降水资料进行质量控制和检验评估.有效地过滤了错误和不可靠的观测记录,构建了一套新的全球台站逐日降水资料集.利用CMAP和GPCP降水产品进行的检验评估表明,新资料集月降水具有良好的频率分布和空间结构一致性.新资料集与CMAP和GPCP降水的全球平均时间相...     

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.     

Are satellite products good proxies for gauge precipitation over Singapore?

The uncertainties in two high-resolution satellite precipitation products (TRMM 3B42 v7.0 and GSMaP v5.222) were investigated by comparing them against rain gauge observations over Singapore on sub-daily scales. The satellite-borne precipitation products are assessed in terms of seasonal, monthly and daily variations, the diurnal cycle, and extreme precipitation over a 10-year period (2000–2010). Results indicate that the uncertainties in extreme precipitation is higher in GSMaP than in TRMM, possibly due to the issues such as satellite merging algorithm, the finer spatio-temporal scale of high intensity precipitation, and the swath time of satellite. Such discrepancies between satellite-borne and gauge-based precipitations at sub-daily scale can possibly lead to distorting analysis of precipitation characteristics and/or application model results. Overall, both satellite products are unable to capture the observed extremes and provide a good agreement with observations only at coarse time scales. Also, the satellite products agree well on the late afternoon maximum and heavier rainfall of gauge-based data in winter season when the Intertropical Convergence Zone (ITCZ) is located over Singapore. However, they do not reproduce the gauge-observed diurnal cycle in summer. The disagreement in summer could be attributed to the dominant satellite overpass time (about 14:00 SGT) later than the diurnal peak time (about 09:00 SGT) of gauge precipitation. From the analyses of extreme precipitation indices, it is inferred that both satellite datasets tend to overestimate the light rain and frequency but underestimate high intensity precipitation and the length of dry spells. This study on quantification of their uncertainty is useful in many aspects especially that these satellite products stand scrutiny over places where there are no good ground data to be compared against. This has serious implications on climate studies as in model evaluations and in particular, climate model simulated future projections, when information on precipitation extremes need to be reliable as they are highly crucial for adaptation and mitigation.     

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.     

基于热带测雨卫星探测的东亚降水云结构特征的研究

利用热带测雨卫星的测雨雷达(TRMMPR)、微波成像仪(TMI)、可见光和红外辐射计(VIRS)、闪电成像仪(LIS)对降水云的综合探测结果,结合全球降水气候计划降水资料(GPCP)和中国气象台站雨量计观测资料,分析了东亚降水分布特点,并比较了TRMMPR与GPCP及地面雨量计观测结果的差异;揭示了中国中东部大陆、东海和南海对流降水和层云降水平均降水廓线的季节变化特征及物理意义,以及TMI高频和低频微波信号对地表降水率变化的响应特点;通过对中尺度强降水系统、锋面气旋降水系统和热对流降水系统的个例分析,探明了降水结构及其与闪电活动的关系、降水云顶部信息与地表雨强之间的关系。     

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.     

Assessing the performance of satellite-based precipitation products and its dependence on topography over Poyang Lake basin

Satellite-based precipitation products (SPPs) have greatly improved their applicability and are expected to offer an alternative to ground-based precipitation estimates in the present and the foreseeable future. There is a strong need for a quantitative evaluation of the usefulness and limitations of SPPs in operational meteorology and hydrology. This study compared two widely used high-resolution SPPs, the Tropical Rainfall Measuring Mission (TRMM) and Precipitation Estimation from Remote Sensing Information using Artificial Neural Network (PERSIANN) in Poyang Lake basin which is located in the middle reach of the Yangtze River in China. The bias of rainfall amount and occurrence frequency under different rainfall intensities and the dependence of SPPs performance on elevation and slope were investigated using different statistical indices. The results revealed that (1) TRMM 3B42 usually underestimates the rainy days and overestimates the average rainfall as well as annual rainfall, while the PERSIANN data were markedly lower than rain gauge data; (2) the rainfall contribution rates were underestimated by TRMM 3B42 in the middle rainfall class but overestimated in the heavy rainfall class, while the opposite trend was observed for PERSIANN; (3) although the temporal distribution characteristics of monthly rainfall were correctly described by both SPPs, PERSIANN tended to suffer a systematic underestimation of rainfall in every month; and (4) the performances of both SPPs had clear dependence on elevation and slope, and their relationships can be fitted using quadratic equations.