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。     

The 183-WSL fast rain rate retrieval algorithm: Part I: Retrieval design

The Water vapour Strong Lines at 183 GHz (183-WSL) fast retrieval method retrieves rain rates and classifies precipitation types for applications in nowcasting and weather monitoring. The retrieval scheme consists of two fast algorithms, over land and over ocean, that use the water vapour absorption lines at 183.31 GHz corresponding to the channels 3 (183.31 ± 1 GHz), 4 (183.31 ± 3 GHz) and 5 (183.31 ± 7 GHz) of the Advanced Microwave Sounding Unit module B (AMSU-B) and of the Microwave Humidity Sounder (MHS) flying on NOAA-15-18 and Metop-A satellite series, respectively.The method retrieves rain rates by exploiting the extinction of radiation due to rain drops following four subsequent steps. After ingesting the satellite data stream, the window channels at 89 and 150 GHz are used to compute scattering-based thresholds and the 183-WSLW module for rainfall area discrimination and precipitation type classification as stratiform or convective on the basis of the thresholds calculated for land/mixed and sea surfaces. The thresholds are based on the brightness temperature difference Δ_win = T_B89 ? T_B150 and are different over land (L) and over sea (S): cloud droplets and water vapour (Δ_win < 3 K L; Δ_win < 0 K S), stratiform rain (3 K < Δ_win < 10 K L; 0 K < Δ_win < 10 K S), and convective rain (Δ_win > 10 K L and S). The thresholds, initially empirically derived from observations, are corroborated by the simulations of the RTTOV radiative transfer model applied to 20000 ECMWF atmospheric profiles at midlatitudes and the use of data from the Nimrod radar network. A snow cover mask and a digital elevation model are used to eliminate false rain area attribution, especially over elevated terrain. A probability of detection logistic function is also applied in the transition region from no-rain to rain adjacent to the clouds to ensure continuity of the rainfall field. Finally, the last step is dedicated to the rain rate retrieval with the modules 183-WSLS (stratiform) and 183WSLC (convective), and the module 183-WSL for total rainfall intensity derivation.A comparison with rainfall retrievals from the Goddard Profiling (GPROF) TRMM 2A12 algorithm is done with good results on a stratiform and hurricane case studies. A comparison is also conducted with the MSG-based Precipitation Index (PI) and the Scattering Index (SI) for a convective-stratiform event showing good agreement with the 183-WSLC retrieval. A complete validation of the product is the subject of Part II of the paper.     

Role of cumulus parameterization schemes in simulating heavy rainfall episodes off the coast of Maharashtra state during 28 June–4 July 2007

Indian summer monsoon gives on an average 250 cm of rainfall due to mesoscale/synoptic scale systems over west coast of India; now-a-days, MM5 model plays a very crucial role in simulating such heavy rainfall episodes like Mumbai (India) on 26 July 2005, which caused devastation through flash floods. The main aim of this study is to simulate such heavy rainfall episodes using three different cumulus parameterization schemes (CPS) namely Kain–Fritsch-1, Anthes–Kuo and Grell and to compare their relative merits in identifying the characteristics of mesoscale systems over 14 stations in coastal Maharashtra state during 28 June–4 July 2007. MM5 control experiment results are analysed for the fields of mean sea level pressure, wind, geopotential height at 850 hPa and rainfall with the above schemes. It is interesting to note that Kain–Fritsch-1 scheme simulates heavy rainfall amount of 48 cm for an observed rainfall of 51 cm in 24 h. The Grell scheme underestimates heavy rainfall episodes, while the Anthes–Kuo scheme is found to over predict rainfall on both temporal and spatial scales. The reason for better performance of KF-1 scheme may be due to inclusion of updrafts and downdrafts. Later the simulated rainfall quantities at 14 stations over study region are validated with both 3B42RT and observed rain gauge data of India Meteorological Department (IMD) and the results are promising. Finally, for the heavy rainfall prediction cases, the best threat score is at 0.25 mm threshold for three CPSs. Thus, this study is a breakthrough in pointing out that the KF-1 experiment has the best skill in predicting heavy rainfall episodes.     

Analysis of rainfall characteristics of the Madden–Julian oscillation using TRMM satellite data

Rainfall characteristics of the Madden–Julian oscillation (MJO) are analyzed primarily using tropical rainfall measuring mission (TRMM) precipitation radar (PR), TRMM microwave imager (TMI) and lighting imaging sensor (LIS) data. Latent heating structure is also examined using latent heating data estimated with the spectral latent heating (SLH) algorithm.The zonal structure, time evolution, and characteristic stages of the MJO precipitation system are described. Stratiform rain fraction increases with the cloud activity, and the amplitude of stratiform rain variation associated with the MJO is larger than that of convective rain by a factor of 1.7. Maximum peaks of both convective rain and stratiform rain precede the minimum peak of the outgoing longwave radiation (OLR) anomaly which is often used as a proxy for the MJO convection. Stratiform rain remains longer than convective rain until ∼4000 km behind the peak of the mature phase. The stratiform rain contribution results in the top-heavy heating profile of the MJO.Associated with the MJO, there are tri-pole convective rain top heights (RTH) at 10–11, ∼7 and ∼3 km, corresponding to the dominance of afternoon showers, organized systems, and shallow convections, respectively. The stratiform rain is basically organized with convective rain, having similar but slightly lower RTH and slightly lags the convective rain maximum. It is notable that relatively moderate (∼7 km) RTH is dominant in the mature phase of the MJO, while very tall rainfall with RTH over 10 km and lightning frequency increase in the suppressed phase. The rain-yield-per flash (RPF) varies about 20–100% of the mean value of ∼2–10 × 10⁹ kg fl⁻¹ over the tropical warm ocean and that of ∼2–5 × 10⁹ kg fl⁻¹ over the equatorial Islands, between the convectively suppressed phase and the active phase of MJO, in the manner that RPF is smaller in the suppressed phase and larger in the active phase.     

A multisource scheme based on NWP and MSG data to correct non-precipitating weather radar echoes

Weather radar quantitative precipitation estimates (QPE) are one of the usual tools to monitor rainfall intensity remotely by forecasters on duty or by automatic systems such as hydrological models. Derivation of radar QPE requires a set of robust quality control procedures to address a number of different factors. In particular, significant departures from the standard temperature and moisture atmospheric vertical profiles may increase dramatically the refraction of the radar beam. This anomalous propagation (AP or anaprop) of the microwave radar energy may therefore increase the number of spurious echoes due to ground clutter and contribute, with non-realistic rainfall, to the estimated precipitation field. Based on previous experience of geostationary satellite imagery usage to depict cloud-free areas in precipitation analysis systems, a methodology to incorporate Meteosat Second Generation (MSG) observations and NWP data in the quality control of weather radar QPE was implemented considering two different algorithms. They were validated with two different verification data sets, built with manually edited radar data and rain gauge observations using HKS, PC and FAR scores. The evaluation of the scores was performed for weak (<15 dBZ), stronger and all echoes and for day, night and day and night conditions. One of the methods dealing with weak echoes at night improved PC from 80 to 97% and decreased FAR from 0.32 to 0.19. The results obtained indicate that the technique shows potential for operational application complementing other existing methodologies designed to improve the quality of weather radar precipitation estimates.     

Variability of the TRMM-PR total and convective and stratiform rain fractions over the Indian region during the summer monsoon

Level 3 (3A25) TRMM Precipitation Radar (PR) data are used for 13 years period (1998–2010) to prepare climatology of TRMM PR derived near surface rain (Total rain) and rain fractions for the 4-months duration of Indian Summer Monsoon season (June–September) as well as for individual months. It is found that the total rain is contributed mostly (99 %) by two rain fractions i.e. stratiform and convective rain fractions for the season as well as on the monthly basis. It is also found that total rain estimates by PR are about 65 % of the gauge measured rain over continental India as well as on sub-regional basis. Inter-annual variability of TRMM-PR rain estimates for India mainland and its sub-regions as well as over the neighboring oceanic regions, in terms of coefficient of variability (CV) is discussed. The heaviest rain region over north Bay of Bengal (BoB) is found to have the lowest CV. Another sub-region of low CV lies over the eastern equatorial Indian ocean (EEIO). The CVs of total rain as well as its two major constituents are found to be higher on monthly basis compared to seasonal basis. Existence of a well known dipole between the EEIO and the north BoB is well recognized in PR data also. Significant variation in PR rainfall is found over continental India between excess and deficit monsoon seasons as well as between excess and deficit rainfall months of July and August. Examination of rainfall fractions between the BoB and Central India on year to year basis shows that compensation in rainfall fractions exists on monthly scale on both the regions. Also on the seasonal and monthly scales, compensation is observed in extreme monsoon seasons between the two regions. However, much less compensation is observed between the north BoB and EEIO belts in extreme rain months. This leads to speculation that the deficit and excess seasons over India may result from slight shift of the rainfall from Central India to the neighboring oceanic regions of north BoB. Contribution of stratiform and convective rain fractions have been also examined and the two fractions are found to contribute almost equally to the total rain. Results are further discussed in terms of the possible impact of the two rain fractions on circulation based on possible difference is vertical profiles of latent heat of two types of rain. Substantial differences in the lower and upper tropospheric circulation regimes are noticed in both deficit and excess monsoon months/seasons, emphasizing the interaction between rainfall (latent heat) and circulation.     

A Mediterranean nocturnal heavy rainfall and tornadic event. Part I: Overview,damage survey and radar analysis

This study presents an analysis of a severe weather case that took place during the early morning of the 2nd of November 2008, when intense convective activity associated with a rapidly evolving low pressure system affected the southern coast of Catalonia (NE Spain). The synoptic framework was dominated by an upper level trough and an associated cold front extending from Gibraltar along the Mediterranean coast of the Iberian Peninsula to SE France, which moved north-eastward. South easterly winds in the north of the Balearic Islands and the coast of Catalonia favoured high values of 0–3 km storm relative helicity which combined with moderate MLCAPE values and high shear favoured the conditions for organized convection. A number of multicell storms and others exhibiting supercell features, as indicated by Doppler radar observations, clustered later in a mesoscale convective system, and moved north-eastwards across Catalonia. They produced ground-level strong damaging wind gusts, an F2 tornado, hail and heavy rainfall. Total lightning activity (intra-cloud and cloud to ground flashes) was also relevant, exhibiting several classical features such as a sudden increased rate before ground level severe damage, as discussed in a companion study. Remarkable surface observations of this event include 24 h precipitation accumulations exceeding 100 mm in four different observatories and 30 minute rainfall amounts up to 40 mm which caused local flash floods. As the convective system evolved northward later that day it also affected SE France causing large hail, ground level damaging wind gusts and heavy rainfall.     

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.     

Evaluation of precipitation prediction skill of IMD operational NWP system over Indian monsoon region

Summary The study provides a concise and synthesized documentation of the current level of skill of the operational NWP model of India Meteorological Department based on daily 24 hours forecast run of the model during two normal monsoon years 2001 and 2003 making detailed inter-comparison with daily rainfall analysis from the use of high dense land rain gauge observations. The study shows that the model, in general, is able to capture three regions of climatologically heavy rainfall domains along Western Ghats, Northeast India and over east central India, over the domain of monsoon trough. However, the accuracy in prediction of location and magnitude of rainfall fluctuates considerably. The inter-comparison reveals that performance of the model rainfall forecast deteriorated in 2003 when rainfall over most parts of the region was significantly under-predicted. These features are also reflected in the error statistics. The study suggests that there is a need to maximize the data ingest in the model with a better data assimilation scheme to improve the rainfall forecast skill.     

GPM IMERG和ERA5降水数据精度在云南复杂地形区域的评估检验

为评估和对比GPM IMERG、ERA5降水数据在云南的适用性,利用2014年4月至2018年6月的地面气象观测数据、GPM IMERG卫星遥感降水产品和ERA5再分析降水数据,采用定量和分类评分7项指标评估GPM IMERG和ERA5日降水产品在云南的适用性。结果表明:2种数据存在小雨日雨量高估,中雨及以上量级雨日雨量低估的问题,ERA5数据更为突出,小雨日居多导致降水整体高估;GPM IMERG数据空、漏报并存,ERA5则高空报、低漏报严重;小雨日较多(较少)的区域2种数据易出现高漏报(空报);不同雨强区间GPM IMERG秋季降水数据精度最高,冬季存在低雨强低估,高雨强高估的不同表现;20mm/d以下中低雨强段上2种降水数据与地面站点数据误差较小,雨强变大,误差增大,雨强大于20mm/d时,2种数据随雨强增大与站点偏差差异更为显著;随坡度和起伏度增大2种降水数据精度呈变差趋势;多项指标评估表明GPM IMERG降水数据在云南具有更高精度。研究结果为应用和开展农业、水利、水文、气象等相关学科研究提供参考依据。     

基于GPM卫星的广东汛期降水日变化特征与评估

新一代全球降水观测计划GPM作为TRMM卫星的继承者,在物理探测和降水反演算法上具有明显进步。以广东省雨量自动站为基准,对2014—2018年间GPM的格点降水估测产品IMERG（V5B）的日变化特征和估测误差进行分析。结果表明,IMERG能清晰反映广东前、后汛期的降水双峰型特征,但对下午降水峰值明显高估,峰值出现时间滞后;而对于沿海早晨峰值降水则明显低估,对于降水极值,低估更加显著。IMERG对两个峰值的估测误差受不同因素影响,下午峰值降水的相对偏差与地形密切相关,珠江三角洲平原为稳定高估区,地形高度越高,低估幅度越大;而早晨峰值降水极值负偏差与地形高度、降水量的相关性均较小。对出现显著负偏差的早晨沿海降水样本日进行925 hPa风场合成,可知IMERG明显低估时,对应区域上游较强的超低空西南气流与风速夜间增长。IMERG对这一季风活动背景降水的低估构成了其估测早晨降水误差的主要来源。      

Local onset and demise of the Indian summer monsoon

This paper introduces an objective definition of local onset and demise of the Indian summer monsoon (ISM) at the native grid of the Indian Meteorological Department’s rainfall analysis based on more than 100 years of rain gauge observations. The variability of the local onset/demise of the ISM is shown to be closely associated with the All India averaged rainfall onset/demise. This association is consistent with the corresponding evolution of the slow large-scale reversals of upper air and ocean variables that raise the hope of predictability of local onset and demise of the ISM. The local onset/demise of the ISM also show robust internannual variations associated with El Nino and the Southern Oscillation and Indian Ocean dipole mode. It is also shown that the early monsoon rains over northeast India has a predictive potential for the following seasonal anomalies of rainfall and seasonal length of the monsoon over rest of India.

     

Increasing trend of northeast monsoon rainfall over the equatorial Indian Ocean and peninsular India

Peninsular India and Sri Lanka receive major part of their annual rainfall during the northeast monsoon season (October–December). The long-term trend in the northeast monsoon rainfall over the Indian Ocean and peninsular India is examined in the vicinity of global warming scenario using the Global Precipitation Climatology Project (GPCP) dataset available for the period 1979–2010. The result shows a significant increasing trend in rainfall rate of about 0.5 mm day^?1 decade^?1 over a large region bounded by 10 °S–10 °N and 55 °E–100 °E. The interannual variability of seasonal rainfall rate over peninsular India using conventional rain gauge data is also investigated in conjunction to the Indian Ocean dipole. The homogeneous rain gauge data developed by Indian Institute of Tropical Meteorology over peninsular India also exhibit the considerable upward rainfall trend of about 0.4 mm day^?1 decade^?1 during this period. The associated outgoing longwave radiation shows coherent decrease in the order of 2 W?m^?2 decade^?1 over the rainfall increase region.     

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.     

Simulation of heavy rainfall events over Indian monsoon region using WRF-3DVAR data assimilation system

We present the results of the impact of the 3D variational data assimilation (3DVAR) system within the Weather Research and Forecasting (WRF) model to simulate three heavy rainfall events (25–28 June 2005, 29–31 July 2004, and 7–9 August 2002) over the Indian monsoon region. For each event, two numerical experiments were performed. In the first experiment, namely the control simulation (CNTL), the low-resolution global analyses are used as the initial and boundary conditions of the model. In the second experiment (3DV-ANA), the model integration was carried out by inserting additional observations in the model’s initial conditions using the 3DVAR scheme. The 3DVAR used surface weather stations, buoy, ship, radiosonde/rawinsonde, and satellite (oceanic surface wind, cloud motion wind, and cloud top temperature) observations obtained from the India Meteorological Department (IMD). After the successful inclusion of additional observational data using the 3DVAR data assimilation technique, the resulting reanalysis was able to successfully reproduce the structure of convective organization as well as prominent synoptic features associated with the mid-tropospheric cyclones (MTC). The location and intensity of the MTC were better simulated in the 3DV-ANA as compared to the CNTL. The results demonstrate that the improved initial conditions of the mesoscale model using 3DVAR enhanced the location and amount of rainfall over the Indian monsoon region. Model verification and statistical skill were assessed with the help of available upper-air sounding data. The objective verification further highlighted the efficiency of the data assimilation system. The improvements in the 3DVAR run are uniformly better as compared to the CNTL run for all the three cases. The mesoscale 3DVAR data assimilation system is not operational in the weather forecasting centers in India and a significant finding in this study is that the assimilation of Indian conventional and non-conventional observation datasets into numerical weather forecast models can help improve the simulation accuracy of meso-convective activities over the Indian monsoon region. Results from the control experiments also highlight that weather and regional climate model simulations with coarse analysis have high uncertainty in simulating heavy rain events over the Indian monsoon region and assimilation approaches, such as the 3DVAR can help reduce this uncertainty.     

Effect of spatial correlation on regional trends in rain events over India

The regional trends are evaluated in the frequency of various rain events using the daily gridded (1°?×?1°) rainfall dataset for the time period 1901–2004, prepared by the India Meteorological Department (IMD). In terms of intensity, the events are classified as low, moderate, heavy and extreme heavy, while short and long spells are classified on the basis of duration of rainfall. The analytical (parametric) and the empirical (bootstrap) techniques were used to incorporate the impact of spatial correlation in regional trends. It is observed that, consideration of spatial correlation reduces the significance level of the trends and the effective number of grid points falling under each category. Especially, the noticeable cross-correlation have reduced the significance of the trends in moderate and long spell rain events to a large extent, while the significance of trends in the extreme heavy and short spell events is not highly affected because of small cross-correlation.     

多普勒天气雷达1 h降水产品的质量评估

为评估多普勒天气雷达1 h降水量(OHP)产品在降水预报中的质量, 从南通新一代多普勒天气雷达的体扫资料及其覆盖范围内太湖流域34个雨量站的同期逐小时降水资料中选取了19个降水个例。以空报率、漏报率、准确预报临界指数、平均误差、平均绝对误差和均方根误差等, 为评估降水预报质量的指标。根据雨量站实测日降水量(P)和1 h降水量(P₁)大小分级, 对不同情况下的OHP进行质量评估。结果表明:(1)OHP的空报率和漏报率较高, 导致整体的准确预报临界指数偏低。(2)与不同的P和P₁量级相对应, 不同的OHP预报质量有明显差异。除P<10 mm时和P₁<1 mm时稍有高估外, 其他量级都存在低估, 且随着P的增加, 空报率和漏报率都减小。(3)对2007年9月18日的降水个例分析表明:OHP与实测的落区基本一致, 但降水强度上有偏差。不同站点的雨强偏差不同, 距南通雷达站100 km左右的OHP误差较小。     

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.     

A new method to compare hourly rainfall between station observations and satellite products over central-eastern China

This study employs a newly defined regional-rainfall-event (RRE) concept to compare the hourly characteristics of warm-season (May-September) rainfall among rain gauge observations, China merged hourly precipitation analysis (CMPA-Hourly), and two commonly used satellite products (TRMM 3B42 and CMORPH). By considering the rainfall characteristics in a given limited area rather than a single point or grid, this method largely eliminates the differences in rainfall characteristics among different observations or measurements over central-eastern China. The results show that the spatial distribution and diurnal variation of RRE frequency and intensity are quite consistent among different datasets, and the performance of CMPA-Hourly is better than the satellite products when compared with station observations. A regional rainfall coefficient (RRC), which can be used to classify local rain and regional rain, is employed to represent the spatial spread of rainfall in the limited region defining the RRE. It is found that rainfall spread in the selected grid box is more uniform during the nocturnal to morning hours over central-eastern China. The RRC tends to reach its diurnal maximum several hours after the RRE intensity peaks, implying an intermediate transition stage from convective to stratiform rainfall. In the afternoon, the RRC reaches its minimum, implying the dominance of local convections on small spatial scale in those hours, which could cause large differences in rain gauge and satellite observations. Since the RRE method reflects the overall features of rainfall in a limited region rather than at a fixed point or in a single grid, the widely recognized overestimation of afternoon rainfall in satellite products is not obvious, and thus the satellite estimates are more reliable in representing sub-daily variation of rainfall from the RRE perspective. This study proposes a reasonable method to compare satellite products with rain gauge observations on the sub-daily scale, which also has great potential to be used in evaluating the spatiotemporal variation of cloud and rainfall in numerical models.     

一次特大暴雨过程中涡度与急流的影响分析

利用自动气象站雨量资料,常规观测资料,FY-2D TBB云图资料以及NCEP再分析资料,对2013年6月30日～7月2日高原低涡东移和西南涡耦合形成的遂宁地区特大暴雨过程进行研究。主要分析了高原低涡和西南涡的耦合对此次强降水的影响以及低空急流演化和强降水的关系。结果表明:当高原涡和西南涡发生耦合时会使得高空低涡发展加强,并激发遂宁暴雨天气的产生。急流为高空低涡的发展提供不稳定能量。超低空急流和低空急流对强降水有提前2小时的指示意义,低空急流指数增大的过程和降水量的强度成正比关系。