Remote Sensing of Precipitation on the Tibetan Plateau Using the TRMM Microwave Imager

Yao, Zhanyu; Li, Wanbiao; Zhu, Yu-Xian; Zhao, Bolin; Chen, Yong

doi:10.1175/1520-0450(2001)040<1381:rsopot>2.0.co;2

Cited by 17 publications

(14 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although these gridded datasets incorporate station observations, for places with low station densities, such as over the Tibetan Plateau, the satellite estimates have the dominant influence. Yao et al (2001) demonstrated the feasibility of using TMI-derived brightness temperature to estimate precipitation with reasonable accuracy over the Tibetan Plateau. In an earlier study (Yin et al 2004), it was found that the SSM/I-based rainfall estimates can be significantly improved by incorporating topographic and geographic location variables.…”

Section: Introductionmentioning

confidence: 98%

An Assessment of the Biases of Satellite Rainfall Estimates over the Tibetan Plateau and Correction Methods Based on Topographic Analysis

Yin

Zhang

Liu

et al. 2008

Journal of Hydrometeorology

138

View full text Add to dashboard Cite

The hydrological processes over the Tibetan Plateau have significant implications on regional macroscale atmospheric circulation patterns and the Asian monsoon system. Because of its remote setting and lack of ground observations, it is difficult to study the spatial and temporal patterns of precipitation over the plateau, and satellite remote sensing technology can be used to fill in the gaps where station data are not available. In this study the authors examine monthly 1°ϫ 1°rainfall estimates obtained from the Special Sensor Microwave Imager (SSM/I) [National Environmental Satellite, Data, and Information Service (NESDIS) algorithm] and Tropical Rainfall Measuring Mission (TRMM) 3B42 version 5 (V5) products for the months of April-October 1998-2002 over the Tibetan Plateau. By comparing the satellite estimates with ground observations at 94 weather stations in the study region, the authors derived regression models that produced significant improvements to satellite estimates based on various levels of correction efforts, using geographic location and topographic variables extracted from digital elevation models using geographic information systems (GIS) technology. The explained variance in observed precipitation was improved from 34% to 38% by SSM/I and TRMM 3B42 V5 products alone to over 70% when location and topographic variables were added. These topographic variables reflect micro-to mesoscale surface roughness, height of topographic features or relief, slopes facing toward or away from the moisture pathways, and relative locations or directions to prominent topographic features such as mountain peaks and ridgelines.

show abstract

Section: Introductionmentioning

confidence: 98%

An Assessment of the Biases of Satellite Rainfall Estimates over the Tibetan Plateau and Correction Methods Based on Topographic Analysis

Yin

Zhang

Liu

et al. 2008

Journal of Hydrometeorology

138

View full text Add to dashboard Cite

show abstract

“…To delineate rain areas over the Tibetan Plateau with a sparse rain gauge network, Yao, Li, Zhu, Zhao and Chen [22] attempted to delineate rain/no rain areas with the TBs measured by TMI using an unsupervised classification method, the dynamic cluster K-means (termed as Kmean, hereafter). In this study, the performance of this method was assessed as a comparison of the new rain area delineation method proposed by this study.…”

Section: Dynamic Cluster K-means Methodsmentioning

confidence: 99%

“…The advantage of this method is that ground observations are not necessary. According to Yao, Li, Zhu, Zhao and Chen [22], the TBs at 19.4, 21.3, 37.0 and 85.5 GHz were used as the inputs of the Kmean method to delineate rain/no rain areas. In this study, the Kmean method was conducted using the "kmeans" toolkit provide by the MATLAB 2009a software.…”

Section: Dynamic Cluster K-means Methodsmentioning

confidence: 99%

“…Therefore, the TD is a promising indicator of rain areas. In this study, the vertically polarized TBs at 37 GHz (high frequency) and 19 GHz (high frequency) were used to calculate TD according to the research of Yao, Li, Zhu, Zhao and Chen [22].…”

Section: Rain Area Delineation Methods Based On Pnnmentioning

confidence: 99%

“…Grody [20] and Ferraro [21] developed the Scatter Index at 85 and 37 GHz to measure the scattering intensity of clouds and eventually retrieved global rain areas with a threshold. Yao et al [22] introduced the dynamic cluster K-means method to delineate rain areas over Tibetan Plateau using multi-channel microwave brightness temperatures. Moreover, several threshold methods based on brightness temperatures at passive microwave (PMW) channels have been proposed to separate rain areas from land surface [23][24][25].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Delineation of Rain Areas with TRMM Microwave Observations Based on PNN

Gonsamo

et al. 2014

Remote Sensing

View full text Add to dashboard Cite

False alarm and misdetected precipitation are prominent drawbacks of high-resolution satellite precipitation datasets, and they usually lead to serious uncertainty in hydrological and meteorological applications. In order to provide accurate rain area delineation for retrieving high-resolution precipitation datasets using satellite microwave observations, a probabilistic neural network (PNN)-based rain area delineation method was developed with rain gauge observations over the Yangtze River Basin and three parameters, including polarization corrected temperature at 85 GHz, difference of brightness temperature at vertically polarized 37 and 19 GHz channels (termed as TB37V and TB19V, respectively) and the sum of TB37V and TB19V derived from the observations of the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI). The PNN method was validated with independent samples, and the performance of this method was compared with dynamic cluster K-means method, TRMM Microwave Imager (TMI) Level 2 Hydrometeor Profile Product and the threshold method used in the Scatter Index (SI), a widely used microwave-based precipitation retrieval algorithm. Independent validation indicated that the PNN method can provide more reasonable rain areas than the other three methods. OPEN ACCESSRemote Sens. 2014, 6 12119 Furthermore, the precipitation volumes estimated by the SI algorithm were significantly improved by substituting the PNN method for the threshold method in the traditional SI algorithm. This study suggests that PNN is a promising way to obtain reasonable rain areas with satellite observations, and the development of an accurate rain area delineation method deserves more attention for improving the accuracy of satellite precipitation datasets.

show abstract

Estimation of heavy rainfall during cyclonic storms from microwave observations using nonlinear approach over Indian Ocean

Mishra

2012

Nat Hazards

View full text Add to dashboard Cite

Remote Sensing of Precipitation on the Tibetan Plateau Using the TRMM Microwave Imager

Cited by 17 publications

References 12 publications

An Assessment of the Biases of Satellite Rainfall Estimates over the Tibetan Plateau and Correction Methods Based on Topographic Analysis

An Assessment of the Biases of Satellite Rainfall Estimates over the Tibetan Plateau and Correction Methods Based on Topographic Analysis

Delineation of Rain Areas with TRMM Microwave Observations Based on PNN

Estimation of heavy rainfall during cyclonic storms from microwave observations using nonlinear approach over Indian Ocean

Contact Info

Product

Resources

About