The post-real time product of Day-1 Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) is evaluated over Mainland China from April to December 2014 at the hourly timescale, against data from hourly ground-based observations. In addition, the IMERG product is compared with its predecessor-the Version-7 post-real-time 3B42 (3B42V7) product of Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) at its original 3-hourly and then daily timescales for the same period. All the products are cross-evaluated at gridded, regional, and national scales. Results show that: (1) the Day-1 IMERG shows appreciably better performance than 3B42V7 at both sub-daily and daily timescales, and all the three spatial scales. The gap between the two products is more significant at the sub-daily resolution; (2) Out of the six sub-regions of China, IMERG especially performs better than 3B42V7 at the mid- and high-latitudes, as well as relatively dry climate regions; (3) IMERG can better reproduce the probability density function (PDF) in terms of precipitation intensity, particularly in the low ranges; and (4) although IMERG better captures the precipitation diurnal variability, both products have room to further improve their capability, particularly in the dry climate and high-latitude regions. This study is among the earliest evaluation and comparison of IMERG and 3B42V7 products, which could be valuable in providing reference for the development of IMERG algorithms, associated global products, and various applications as well.

[1] This paper compares four selected characteristics (mean annual rainfall; mean number of wet days in the year; mean number of days with 2mm or more of rain; and mean 95% quantile of daily rainfall) computed from the Brazilian rain gauge network in the Amazon region, and from the satellite-derived data sets TRMM 3B42 and CMORPH. Comparisons were made at 488 sites over the years 2003 2005, the period for which all three sources provide data, and rainfall characteristics were calculated only from those days in the year with data from all three sources. TRMM and rain gauges mean annual rainfalls were fairly similar, but CMORPH estimates were greater than either, although the statistical significance of the differences were greatly reduced when spatial correlation was allowed for. However, differences between the mean numbers of days with rain (and those with 2mm or more) calculated from the three sources, were very marked, and the differences usually persisted when the errors in the differences included the effects of spatial correlation. Mean 95% quantile of daily rainfall calculated from CMORPH and from rain gauges were shown to be very similar. Over all 488 sites, however, mean annual rainfall calculated from CMORPH was considerably greater than that calculated from rain gauge records, and this result was explained by an analysis of daily rainfalls that exceeded their 95% quantile. The paper emphasizes that, unless spatial correlation is allowed for, uncertainty in rainfall characteristics will be under-estimated, and the apparent statistical significance of differences between values obtained from alternative data sets will be over-estimated.

A large-sample comparison between radar precipitation estimates and ground observations from a high-resolution gauge network reveals distinct improvements achieved by the modifications of the past 8 years. The verification takes into account all days of the period 1997-2004 and gauge locations spread over all of Switzerland. Particular attention was paid to the definition of a set of meaningful and robust quality parameters. This rigorous and systematic verification approach may become a standard for objective judgement of radar performance. Copyright 2006 Royal Meteorological Society

This paper investigates a multicomponent radar-based rainfall estimation algorithm that includes optimum parameter estimation and error correction schemes associated with radar operation over mountainous terrain. Algorithm preprocessing steps include correction for terrain blocking, adjustment for rain attenuation, and interpolation of reflectivity data from polar radar coordinates to a three-level (1, 2, and 3 km) vertically integrated Cartesian grid. The error correction schemes investigated herein include a simple but efficient approach to correct for the vertical variation of reflectivity and a stochastic filtering approach for mean-field radar-rainfall bias adjustment. The primary algorithm parameters are estimated through a global optimization scheme. Eight major flood-inducing storm events observed coincidentally by a C-band weather radar and 39 rain gauge stations over an alpine region of northeast Italy are used. We describe sensitivity analysis of the parameter values obtained from global optimization, the improvements in accuracy owing to the implementation of the different preprocessing and error correction schemes, and the overall improvement achieved as compared with previous algorithm studies in the area. The advantages of performing vertical integration versus using the lowest-available-elevation radar field, applying stochastic filtering versus the deterministic approach for mean field bias, and estimating the algorithm parameters through optimization are demonstrated.

在地形复杂的川渝地区,利用72 个气象站点的实测降水数据对TRMM( Tropical Rainfall MeasurementMission) 卫星降水数据的精度分别在年、季和月尺度上进行验证,并进一步分析了高程和坡度对月尺度验证结果的影响,同时借助主成分分析法,对比了高程与坡度对TRMM 3B43 降水数据的影响程度。结果表明:TRMM3B43 估算的年降水量在川渝地区平均偏高5.38%,其中西部高原区估算结果比中东部地区相对精确。TRMM3B43 与气象站点的季尺度降水数据拟合优度较高,但各季拟合优度之间存在差异,其中春季降水的拟合优度高于其余季节。而TRMM 3B43 的月数据与站点实测降水数据拟合优度<i>R</i>²=0.7615,相关系数<i>R</i>=0.87,表明两者之间相关性显著,数据精度较高;就单个站点而言,大部分站点相关系数较高,误差较小,但叙永站相关系数相对较低,小金、雅安、雷波、盐源以及攀枝花站点数据误差相对较大。高程对数据精度的影响较坡度大,且呈现三次非线性回归的变化特征,随着高程的升高,<i>R</i>呈现增大的变化趋势,|<i>Bias</i>(%)|则呈现出增大—减小—增大的变化趋势;坡度对降水数据精度的影响相对较小且变化规律也较复杂,整体上随坡度变化,TRMM 3B43 数据精度受到明显的影响。

Precipitation affects many aspects of our everyday life. It is the primary source of freshwater and has significant socioeconomic impacts resulting from natural hazards such as hurricanes, floods, droughts, and landslides. Fundamentally, precipitation is a critical component of the global water and energy cycle that governs the weather, climate, and ecological systems. Accurate and timely knowledge of when, where, and how much it rains or snows is essential for understanding how the Earth system functions and for improving the prediction of weather, climate, freshwater resources, and natural hazard events. The Global Precipitation Measurement (GPM) mission is an international satellite mission specifically designed to set a new standard for the measurement of precipitation from space and to provide a new generation of global rainfall and snowfall observations in all parts of the world every 3 h. The National Aeronautics and Space Administration (NASA) and the Japan Aerospace and Exploration Agency (JAXA) successfully launched the Core Observatory satellite on 28 February 2014 carrying advanced radar and radiometer systems to serve as a precipitation physics observatory. This will serve as a transfer standard for improving the accuracy and consistency of precipitation measurements from a constellation of research and operational satellites provided by a consortium of international partners. GPM will provide key measurements for understanding the global water and energy cycle in a changing climate as well as timely information useful for a range of regional and global societal applications such as numerical weather prediction, natural hazard monitoring, freshwater resource management, and crop forecasting.

<p>GPM降水计划是继TRMM之后新一代全球卫星降水产品,其核心观测平台已于2014年2月28日发射,卫星群目前由10颗卫星组成,未来还有可能继续扩充。但目前国内还缺少专门介绍GPM计划及其产品的最新文章。新一代GPM降水产品分为4级,与以往的卫星降水产品相比具有更高的精度、更大的覆盖范围、更高时空分辨率,能够提供全球范围基于微波的3 h以内以及基于微波红外IMERG算法的30 min的雨雪数据产品,有利于促进水文、气象、农业和灾害等学科的研究和应用。与着重观测热带亚热带地区降水的TRMM相比,GPM能够更加精确地捕捉微量降水(&lt;0.5 mm&middot;h^-1)和固态降水,这两种类型降水的观测对中高纬度地区和高原地区具有重要意义。对GPM计划的核心观测平台、算法产品、地面验证和应用前景等进行了阐述,有利于GPM产品在国内的应用和推广。</p>

61Gauge-adjusted IMERG_Cal greatly outperforms real-time IMERG_Uncal and is slightly better than 3B42V7 over mainland China.61IMERG_Uncal, IMERG_Cal and 3B42V7 show poor performances in detecting precipitation in winter.61Great overestimation or underestimation errors are found in IMERG_Uncal over most mainland China.

ABSTRACT Through optimizing the daily precipitation climatology, a new high-resolution (0.25° × 0.25° lat./lon.) gridded daily precipitation analysis over Mainland China was developed based on the optimal interpolation (OI) method. This product, based on about 2400 gauge stations over Mainland China from 1955 to the present, is called the China Gauge-based Daily Precipitation Analysis (CGDPA). In this study, using independent precipitation observations as the benchmark, CGDPA and the Climate Prediction Center Unified gauge dataset (CPC_UNI) from the National Oceanic and Atmospheric Administration (NOAA) are validated from May to September of 2008–2010 on a 0.5° × 0.5° lat./lon. grid. The CGDPA has smaller bias and root mean square error, and higher spatial correlation with the validation data than CPC_UNI. Further investigation indicates that this improvement is mainly owing to the larger number of gauges used in the CGDPA. The East Asia gauge analysis (EA_Gauge) is also introduced to comparatively evaluate the capabilities of monitoring precipitation events with different rainfall rates over Mainland China. CGDPA can capture more strong rainfall events while CPC_UNI and EA_Gauge tend to smooth the precipitation structure and miss more local strong rainfall events with precipitation larger than 25 mm day611 over Mainland China. The long-term precipitation time series described by the CGDPA and EA_Gauge agree very well while CPC_UNI substantially underestimates precipitation especially over the sparse-gauged regions and after 1982. CGDPA is thus suggested as a readily available input to applications over Mainland China, whenever possible.

[1] Satellite-based precipitation estimates have great potential for a wide range of critical applications, but their error characteristics need to be examined and understood. In this study, six (6) high-resolution, satellite-based precipitation data sets are evaluated over the contiguous United States against a gauge-based product. An error decomposition scheme is devised to separate the errors into three independent components, hit bias, missed precipitation, and false precipitation, to better track the error sources associated with the satellite retrieval processes. Our analysis reveals the following. (1) The three components for each product are all substantial, with large spatial and temporal variations. (2) The amplitude of individual components sometimes is larger than that of the total errors. In such cases, the smaller total errors are resulting from the three components canceling one another. (3) All the products detected strong precipitation (>40 mm/d) well, but with various biases. They tend to overestimate in summer and underestimate in winter, by as much as 50% in either season, and they all miss a significant amount of light precipitation (<10 mm/d), up to 40%. (4) Hit bias and missed precipitation are the two leading error sources. In summer, positive hit bias, up to 50%, dominates the total errors for most products. (5) In winter, missed precipitation over mountainous regions and the northeast, presumably snowfall, poses a common challenge to all the data sets. On the basis of the findings, we recommend that future efforts focus on reducing hit bias, adding snowfall retrievals, and improving methods for combining gauge and satellite data. Strategies for future studies to establish better links between the errors in the end products and the upstream data sources are also proposed.