An examination of summer precipitation over Asia based on TRMM/TMI

A 6-year dataset of summer monthly mean precipitation derived from Tropical Precipitation Measure-ment Mission (TRMM)-Microwave Imager (TMI) was used to delineate the spatial distribution patterns of precipitation throughout Asian areas, which indicates that there are three rainfall centers located at the northern coast of the Bay of Bengal, the South China Sea and the western equatorial Pacific Warm Pool, respectively. Based upon the analysis of horizontal distribution, the capability of TMI for characterizing terrestrial and maritime precipitation has been evaluated and compared with Global Precipitation Climatology Project (GPCP) dataset. It was found that TMI and GPCP are well consistent with each other, while a few significant differences occur at several regions over land. By investigating rainfall esti-mates over six specific locations in Asia, a systematic underestimation of TMI was demonstrated, which could be explained by the inherent deficiency within TMI terrestrial algorithm relying on scat-tering signal from ice particles in a precipitation system. A further analysis shows that the highly in-homogeneous distribution of rain gauges employed by GPCP contributes a great deal to the significant discrepancy between GPCP and TMI, especially over regions surrounding the Tibetan Plateau where rain gauges are quite scarce.     

Evaluating the suitability of TRMM satellite rainfall data for hydrological simulation using a distributed hydrological model in the Weihe River catchment in China

The objective of this study is to quantitatively evaluate Tropical Rainfall Measuring Mission (TRMM) data with rain gauge data and further to use this TRMM data to drive a Distributed Time-Variant Gain Model (DTVGM) to perform hydrological simulations in the semi-humid Weihe River catchment in China. Before the simulations, a comparison with a 10-year (2001-2010) daily rain gauge data set reveals that, at daily time step, TRMM rainfall data are better at capturing rain occurrence and mean values than rainfall extremes. On a monthly time scale, good linear relationships between TRMM and rain gauge rainfall data are found, with determination coefficients R² varying between 0.78 and 0.89 for the individual stations. Subsequent simulation results of seven years (2001-2007) of data on daily hydrological processes confirm that the DTVGM when calibrated by rain gauge data performs better than when calibrated by TRMM data, but the performance of the simulation driven by TRMM data is better than that driven by gauge data on a monthly time scale. The results thus suggest that TRMM rainfall data are more suitable for monthly streamflow simulation in the study area, and that, when the effects of recalibration and the results for water balance components are also taken into account, the TRMM 3B42-V7 product has the potential to perform well in similar basins.     

澜沧江及周边流域TRMM3B43数据精度检验

在地形复杂的澜沧江及周边流域,利用相关系数法、散点斜率法,以研究区内35个国家基准与基本站观测数据为“真值”对1998-2009年之间月尺度的TRMM 3B43降水数据精度进行检验,采用泰森多边形法、K-Means聚类法分析了高程与坡度对检验结果的影响,借助主成分法比较了高程与坡度对TRMM 3B43的影响程度.研究表...     

A Geo‐spatial study for analysing temporal responses of NDVI to rainfall

Climate change has become a serious concern worldwide owing to its multifaceted impact upon the physical as well as socio‐economic environment (IPCC, 2013). Vulnerability to climate change is much higher in the developing countries like India, where the economy is mainly agro‐based and productivity from the agricultural sector is dependent upon summer monsoon rainfall. Hence, assessing the quantitative relationship between vegetation patterns and climatic influence has become an increasingly important study conducted on regional and global scales. As vegetation cover plays a key role in conserving the natural environment, studying the spatio‐temporal trend of vegetation is crucial in identifying changes in the natural environment. We analysed the spatial responses of SPOT‐VGT NDVI to TRMM based rainfall during a sixteen year period (1998–2013) in the Bundelkhand region of Central India. The Normalized Difference Vegetation Index (NDVI) has proven to be a strong indicator of global vegetation productivity. Among climatic factors, rainfall robustly influences both spatial and temporal outline of NDVI. In this study, we used linear regression for analysing the statistical relationship among NDVI and rainfall and their trends. The study reveals a varying pattern of vegetation dynamics in response to rainfall over the area.     

TRMM降水资料在青藏高原的适用性分析

利用32个观测台站降水资料,在江河源区以均方根误差(Nrmse)、相对误差(BIAS)和相关系数等指标对TRMM降水数据精度进行了评估。结果表明,TRMM降水数据有较好的适用性,与观测数据相比误差在偏负10%以内,在月时间尺度上两者相关系数达到0.9以上。把TRMM数据应用到整个青藏高原,给出了整个高原1998～2009年降水的年均、季均、月均空间分布。降水从东南向西北逐渐递减,东南部年降水量达到1000mm/a,而西北部仅为200mm/a左右,特别是北缘低于100mm/a;降水主要集中在5～9月,冬季降水很少。     

Sustainable fresh water resources management in northern Kuwait—A remote sensing view from Raudatain basin

The paper presents time and cost effective remote sensing technology to estimate recharge potential of fresh water shallow aquifers for their sustainable management of water resources in arid ecosystem. Precipitation measurement of Raudatain basin in Kuwait from TRMM data has been made and integrated with geological, geomorphological and hyrological data, to estimate the recharge potential of this basin. The total precipitation in the basin is estimated at 334 MCM for the year 2003. The initial losses are estimated at 78.43% and the transmission loss at 14.866% of the net precipitation.For sustainable management of the ground water resources, recharge wells have been proposed in the higher order streams to augment the Raudatain aquifer in Kuwait. If the available quantity of precipitation can be successfully utilized, it will reduce considerable pressure on desalination, which leads to increased salinity at the coast in Arabian Gulf.     

Relation between GRACE-derived surface mass variations and precipitation over Australia

Surface mass changes (SMCs) obtained from time-variable gravity observations of the Gravity Recovery and Climate Experiment (GRACE) satellite mission and precipitation data from the Australian Bureau of Metrology and the Tropical Rainfall Measurement Mission are analysed over the Australian continent to determine whether there is a statistically significant correlation between them. The multiple linear regression analysis and the principal-component analysis techniques are applied in order to reveal the spatial and temporal variability of each data set separately as well as their mutual relationships. The study provides results and their statistical significance for the whole of Australia including the Murray Darling Basin in the southeast. The results suggest a significant decrease in water storage in the southeast of Australia and a dominant annual cycle over the majority of the continent for the four year period considered (January 2003 to December 2006), both in the surface mass and rainfall time series. The study revealed a direct relation between the data sets over most parts of Australia as confirmed by visual comparison and correlation analysis. When compared with precipitation data GRACE-derived SMCs exhibit smoother spatial and temporal variations. The latter is better suited to detect long-term trends in the presence of strong annual signals, which can adversely affect long-term trend estimates. Results regarding the magnitude of the annual signal suggest that only about a fourth of the precipitation's water masses remain sufficiently long in an area to be detected as a gravity change. The respective phases of the annual signals show an average time lag of about 40 days between precipitation and SMCs, suggesting that it takes about one to two months until a temporal gravity observation can detect a precipitation event.     

The contribution of tropical cyclones to rainfall in Mexico

Investigating the contribution of tropical cyclones to the terrestrial water cycle can help quantify the benefits and hazards caused by the rainfall generated from this type of hydro-meteorological event. Rainfall induced by tropical cyclones can enhance both flood risk and groundwater recharge, and it is therefore important to characterise its minimum, mean and maximum contributions to a region or country’s water balance. This work evaluates the rainfall contribution of tropical depressions, storms and hurricanes across Mexico from 1998 to 2013 using the satellite-derived precipitation dataset TMPA 3B42. Additionally, the sensitivity of rainfall to other datasets was assessed: the national rain gauge observation network, real-time satellite rainfall and a merged product that combines rain gauges with non-calibrated space-borne rainfall measurements. The lower Baja California peninsula had the highest contribution from cyclonic rainfall in relative terms (∼40% of its total annual rainfall), whereas the contributions in the rest of the country showed a low-to-medium dependence on tropical cyclones, with mean values ranging from 0% to 20%. In quantitative terms, southern regions of Mexico can receive more than 2400 mm of cyclonic rainfall during years with significant TC activity. Moreover, (a) the number of tropical cyclones impacting Mexico has been significantly increasing since 1998, but cyclonic contributions in relative and quantitative terms have not been increasing, and (b) wind speed and rainfall intensity during cyclones are not highly correlated. Future work should evaluate the impacts of such contributions on surface and groundwater hydrological processes and connect the knowledge gaps between the magnitude of tropical cyclones, flood hazards, and economic losses.     

How do GPM and TRMM precipitation products perform in alpine regions? A case study in northwestern China’s Qilian Mountains

Journal of Geographical Sciences - Satellite technologies provide valuable areal precipitation datasets in alpine mountains. However, coarse resolution still limits the use of satellite...     

Comparing the spatial extent of Atlantic basin tropical cyclone wind and rain fields prior to land interaction

Understanding changes in the size of tropical cyclone (TC) wind and rain fields before landfall can improve identification of areas that may experience damage. We examine 25 Atlantic basin TCs for 36 h before gale-force winds (R17) cross land. Rain field extents are measured from satellite estimates of rain rates using a Geographic Information System. In each quadrant, R17 is obtained from the Extended Best Track data-set and correlated with the extent of the rain field. In general, both fields expand prior to landfall. The non-linearity of this trend poses problems for persistence forecast models. The largest wind fields are located over the Atlantic Ocean. Correlations between wind and rain field extent are strongly positive for Atlantic cases regardless of whether extratropical transition (ET) occurs and are associated with the direction of vertical wind shear. Poor correlations exist for Gulf observations. Rain fields extend farther towards the east during ET when vertical wind shear is stronger, but wind fields are not significantly different when separating cases based on whether or not ET occurs. As rain fields extend farther than wind fields in 33% of Gulf cases, moderately heavy rainfall may commence before damaging winds arrive, decreasing the time available for preparedness activities.