The hydrological cycle in the ECMWF short range forecasts

Precipitation and latent heat flux forecasts by the European Centre for Medium Range Weather Forecasts (ECMWF) model have been compared with other estimates of these quantities. In the Northern Hemisphere extra-tropics the latent heat flux over oceans and the precipitation over continents in the short range forecasts are probably good estimates of the truth. The day-to-day as well as the interannual variability in these latitudes seem to be realistic.In the Southern Hemisphere extra-tropics there is a strong spin-up in the precipitation forecasts probably with too low precipitation amounts in the short range forecasts. It is speculated that inconsistent use of satellite data leads to a weakening of large-scale rising motions between 40 and 60°S. Also the latent heat flux in these latitudes is probably too low due to a too moist 1000 mb humidity analysis.Over subtropical deserts the precipitation amounts in the forecasts agree with climatological estimates. Contrary to climatological estimates this precipitation is not evaporated but runs off.In the tropics, especially over mountainous areas, the short range forecasts (average for the first 24 h) with the present model tend to overpredict precipitation amounts, but still with reasonable distributions. Averages between days 1 and 2 probably give a good estimate of the truth except over the eastern Pacific where there is an overestimation, also in the medium range forecasts. Strong underestimation of latent heat fluxes over tropical oceans in the short range forecasts have been considerably reduced with a recent model change. There are still areas, e.g. the Southern Hemisphere subtropical Pacific, with too low evaporation due to too moist 1000 mb analyses probably in connection with an inconsistent use of satellite observations.The interannual variability of monthly mean evaporation and precipitation in the short range forecasts reflects partly atmospheric anomalies, but especially in the tropics, and also larger amplitude variations due to changes in the analysis/forecasting scheme.     

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.     

Relationship between rainfall and lightning over central Indian region in monsoon and premonsoon seasons

Lightning activity and rainfall over the central Indian region (lat, 15.5° N to 25.5° N and lon, 75° E to 85° E) from the TRMM satellite have been analyzed. Ten years' data of monthly lightning and hourly averaged monthly rainfall from 1998 to 2007 have been used for analysis, which shows quite different relationships between lightning and rainfall in monsoon and premonsoon seasons in this region. Very good positive correlation is observed between rainfall and lightning during the premonsoon period, however, in the monsoon period a correlation between them is not so good. The different relationship between lightning and rainfall in the monsoon and premonsoon has been attributed to the low updraft during the monsoon period due to low cloud base height and low aerosol concentration during this period. This analysis shows that deep electrified convective systems do form over the central Indian region during active monsoon periods; however the relationship between convective rainfall and lightning frequency during this period is not as consistent as during the premonsoon period.     

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.     

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

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

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。     

江西致洪暴雨天气特征分析与流域洪涝预报研究

在对1959～1990年的资料进行大量普查分析和统计的基础上,指出对流层中低层形势特征在江西致洪暴雨中的特殊重要性,分析了各个天气系统对形成致洪暴雨的作用,并根据中低层天气形势特征来分型建立致洪暴雨的预报模式。应用水文上降水产生流量过程线的变化原理,提出了仅用降水资料来推算流域洪涝指数,用量化指标来预报未来流域洪涝强度的研究思路和方法。该方法思路是利用流域内测站雨量计算出流域的有效综合面雨量(考虑了前一段时间内的逐日流域面雨量的不同贡献)。复核流量(或水位)等洪涝资料与流域有效综合面雨量的关系,最终确定出各级洪涝指数的流域有效综合面雨量的大小,确定洪涝等级。     

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.     

Impact of 1/8° to 1/64° resolution on Gulf Stream model–data comparisons in basin-scale subtropical Atlantic Ocean models

We investigate the impact of 1/8°, 1/16°, 1/32°, and 1/64° ocean model resolution on model–data comparisons for the Gulf Stream system mainly between the Florida Straits and the Grand Banks. This includes mean flow and variability, the Gulf Stream pathway, the associated nonlinear recirculation gyres, the large-scale C-shape of the subtropical gyre and the abyssal circulation. A nonlinear isopycnal, free surface model covering the Atlantic from 9°N to 47°N or 51°N, including the Caribbean and Gulf of Mexico, and a similar 1/16° global model are used. The models are forced by winds and by a global thermohaline component via ports in the model boundaries. When calculated using realistic wind forcing and Atlantic model boundaries, linear simulations with Munk western boundary layers and a Sverdrup interior show two unrealistic mean Gulf Stream pathways between Cape Hatteras and the Grand Banks, one proceeding due east from Cape Hatteras and a second one continuing northward along the western boundary until forced eastward by the regional northern boundary. The northern pathway is augmented when a linear version of the upper ocean global thermohaline contribution to the Gulf Stream is added as a Munk western boundary layer. A major change is required to obtain a realistic pathway in nonlinear models. Resolution of 1/8° is eddy-resolving but mainly gives a wiggly version of the linear model Gulf Stream pathway and weak abyssal flows except for the deep western boundary current (DWBC) forced by ports in the model boundaries. All of the higher resolution simulations show major improvement over the linear and 1/8° nonlinear simulations. Additional major improvement is seen with the increase from 1/16° to 1/32° resolution and modest improvement with a further increase to 1/64°. The improvements include (1) realistic separation of the Gulf Stream from the coast at Cape Hatteras and a realistic Gulf Stream pathway between Cape Hatteras and the Grand Banks based on comparisons with Gulf Stream pathways from satellite IR and from GEOSAT and TOPEX/Poseidon altimetry (but 1/32° resolution was required for robust results), (2) realistic eastern and western nonlinear recirculation gyres (which contribute to the large-scale C-shape of the subtropical gyre) based on comparisons with mean surface dynamic height from the generalized digital environmental model (GDEM) oceanic climatology and from the pattern and amplitude of sea surface height (SSH) variability surrounding the eastern gyre as seen in TOPEX/Poseidon altimetry, (3) realistic upper ocean and DWBC transports based on several types of measurements, (4) patterns and amplitude of SSH variability which are generally realistic compared to TOPEX/Poseidon altimetry, but which vary from simulation to simulation for specific features and which are most realistic overall in the 1/64° simulation, (5) a basin wide explosion in the number and strength of mesoscale eddies (with warm core rings (WCRs) north of the Gulf Stream, the regional eddy features best observed by satellite IR), (6) realistic statistics for WCRs north of the Gulf Stream based on comparison to IR analyses (low at 1/16° resolution and most realistic at 1/64° resolution for mean population and rings generated/year; realistic ring diameters at all resolutions), and (7) realistic patterns and amplitude of abyssal eddy kinetic energy (EKE) in comparison to historical measurements from current meters.     

概率密度匹配法对中国区域卫星降水资料的改进

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

Influence of the Eurasian snow cover on the Indian summer monsoon variability in observed climatologies and CMIP3 simulations

The present study is aimed at revisiting the possible influence of the winter/spring Eurasian snow cover on the subsequent Indian summer precipitation using several statistical tools including a maximum covariance analysis. The snow–monsoon relationship is explored using both satellite observations of snow cover and in situ measurements of snow depth, but also a subset of global coupled ocean–atmosphere simulations from the phase 3 of the Coupled Model Intercomparison Project (CMIP3) database. In keeping with former studies, the observations suggest a link between an east–west snow dipole over Eurasia and the Indian summer monsoon precipitation. However, our results indicate that this relationship is neither statistically significant nor stationary over the last 40 years. Moreover, the strongest signal appears over eastern Eurasia and is not consistent with the Blanford hypothesis whereby more snow should lead to a weaker monsoon. The twentieth century CMIP3 simulations provide longer timeseries to look for robust snow–monsoon relationships. The maximum covariance analysis indicates that some models do show an apparent influence of the Eurasian snow cover on the Indian summer monsoon precipitation, but the patterns are not the same as in the observations. Moreover, the apparent snow–monsoon relationship generally denotes a too strong El Niño-Southern Oscillation teleconnection with both winter snow cover and summer monsoon rainfall rather than a direct influence of the Eurasian snow cover on the Indian monsoon.     

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.     

FGOALS-g2模式模拟和预估的全球季风区极端降水及其变化

利用LASG/IAP（中国科学院大气物理研究所大气科学和地球流体力学数值模拟国家重点实验室）全球耦合模式FGOALS-g2,评估了其对全球季风区极端气候指标的模拟能力,并讨论了RCP8.5排放情景下21世纪季风区极端气候指标的变化特征。总体而言,模式对季风区总降水和极端气候指标1997～2014年气候态和年际变率的空间分布均具有一定的模拟能力。偏差主要表现在模式低估了亚洲季风强降水中心,低估了中雨（10～20 mm d-1）和大雨（20～50 mm d-1）的频率而高估了暴雨（>50 mm d-1）频率。在RCP8.5排放情景下,由于可降水量的增加,模式预估的全球季风区极端降水、降水总量和降水强度将持续增加。到2076～2095年,极端降水和降水强度在北美季风区增加最显著（约22%和17%）,降水总量在澳大利亚增加最显著（约37%）。然而,FGOALS-g2对全球季风区平均的日降水量低于1 mm的连续最大天数（CDD）的预估变化不显著,这是由于预估的CDD在陆地季风区将增加,而在海洋季风区将减少。对各子季风区的分析显示,CDD在南美季风区变长最显著,达到30%,在澳洲季风区变短最显著,达到40%,这与两季风区日降水量低于1 mm的降水事件发生频率变化不同有关。     

Validation of the Satellite-Derived Rainfall Estimates over the Tibet

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

Greenland surface mass balance simulated by a regional climate model and comparison with satellite-derived data in 1990–1991

The 1990 and 1991 ablation seasons over Greenland are simulated with a coupled atmosphere-snow regional climate model with a 25-km horizontal resolution. The simulated snow water content allows a direct comparison with the satellite-derived melt signal. The model is forced with 6-hourly ERA-40 reanalysis at its boundaries. An evaluation of the simulated precipitation and a comparison of the modelled melt zone and the surface albedo with remote sensing observations are presented. Both the distribution and quantity of the simulated precipitation agree with observations from coastal weather stations, estimates from other models and the ERA-40 reanalysis. There are overestimations along the steep eastern coast, which are most likely due to the “topographic barrier effect”. The simulated extent and time evolution of the wet snow zone compare generally well with satellite-derived data, except during rainfall events on the ice sheet and because of a bias in the passive microwave retrieved melt signal. Although satellite-based surface albedo retrieval is only valid in the case of clear sky, the interpolation and the correction of these data enable us to validate the simulated albedo on the scale of the whole Greenland. These two comparisons highlight a large sensitivity of the remote sensing observations to weather conditions. Our high-resolution climate model was used to improve the retrieval algorithms by taking more fully into account the atmosphere variability. Finally, the good agreement of the simulated melting surface with the improved satellite signal allows a detailed estimation of the melting volume from the simulation.     

BALTEX 1/6-degree daily precipitation climatology 1996–1998

Summary A precipitation correction and analysis (PCA) model has been designed and tested during the preparation phase of the BALTEX main experiment BRIDGE. The PCA model consists of a dynamical bias correction module and a geostatistical module. The bias correction reduces the systematic undercatch of the rain gauges due to wind-induced, evaporation and wetting losses by taking instrument-specific properties plus additional information from synoptic observations into account; the mean correction factor is maximum in February (1.25–1.50) and minimum in August (1.02–1.05). The geostatistical module is an ordinary block kriging algorithm; it yields gridded daily precipitation values plus error estimates on the 1/6-degree resolution of the meso-scale BALTEX models. Here we use 3 years (1996–1998) of 4000 rain gauge observations collected by the BALTEX Meteorological Data Centre for a preliminary high-resolution climatology of the BALTEX catchment. It comprises: Time-series of area-averaged daily precipitation; and area distributions of monthly and annual precipitation. Largely independent is the worldwide monthly GPCP data set which includes also satellite data and is available from 1979 on. GPCP yields, for the years 1996–1998, an average of 2.10 mm/day while our evaluation yields 2.01 mm/day. The maximum difference (22%) occurs in January; during the summer months the values are about equal. Received September 27, 2000 Revised February 6, 2001     

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

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

Water vapour flux divergence over the Arabian Sea during 1987 summer monsoon using satellite data

The evaporation rates over the Arabian Sea (AS) for the summer monsoon months (June to September) of 1987 have been computed using the bulk-aerodynamic formula. The satellite derived precipitation from the INSAT-1B VHRR (Very High Resolution Radiometer) sensor operating in the wavelength 10.5–12.5 m has been used for computing the precipitation over the AS. The net water vapour flux divergence (NFD) over AS has been computed as the difference between evaporation and precipitation. The estimates being -0.02 × 10¹⁰, 2.55 × 10¹⁰, 0.70 × 10¹⁰ and 0.44 × 10¹⁰ tons/day respectively for the months June, July, August and September. The NFD over AS was found to be positively and significantly correlated with the mean monsoon rainfall along the west coast of India.     

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.     

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.