Satellite estimates and subpixel variability of rainfall in a semi-arid grassland

Uncertainties in satellite rainfall estimation may derive from both the local rainfall characteristics and its subpixel variability.To study this issue,Micro Rain Radars and a rain gauge network were deployed within a 9-km satel-lite pixel in the semi-arid Xilingol grassland of China in summer 2009.The authors characterized the subpixel variability with the coefficient of variation(CV)and evaluated the satellite rainfall estimation for this semi-arid area.The results showed that rainfall events with a high CV were mostly convective with a small amount of rain-fall.Spatially inhomogeneous rainfall was most likely to occur at the edges of small clouds producing rain.The performance of the TRMM(Tropical Rainfall Measuring Mission)3B42V7 product for daily rainfall was better than that of the CMORPH(Climate Prediction Center morphing technique)and PERSIANN(Precipitation Estima-tion from Remotely Sensed Information Using Artificial Neural Networks)products,although the TRMM product tended to overestimate rainfall in a lake area of the semi-arid grassland.

ESTIMATION OF VERTICAL DISTRIBUTION OF RELATIVE HUMIDITY USING SATELLITE DATA

In this paper, an attempt has been made to find out the vertical distribution of RH at levels of 850, 700 and 500 hPa by using satellite-derived radiation parameters (i.e., albedo, outgoing longwave fluxes, absorb- ed solar radiation and net radiation). For this purpose, multiple regression equations are derived from MONEX-79 upsonde and dropsonde data over the Arabian Sea for the period 11--20 June 1979. Satellite- estimated RH fields have been compared with ECMWF RH fields obtained from FGGE level ⅢB data. The RMS error and error variance for satellite-estimated RH fields have been found to be less than for those of ECMWF. Satellite-estimated isohygric patterns show good agreement with the cloudiness patterns of GOES satellite, whereas ECMWF isohygric patterns do not show much resemblance with the cloudiness patterns. The results of the study suggest that satellite-estimated RH fields could be more useful than ECMWF RH fields and they can be used with some confidence in NWP models.

Accuracy and spatio-temporal variation of high resolution satellite rainfall estimate over the Ganjiang River Basin

Based on spatial interpolation rainfall of the ground gauge measurement,we proposed a method to comprehensively evaluate and compare the accuracy of satellite rainfall estimates (SREs) at three spatial scales:0.25°×0.25° grid scale,sub-catchment scale and the whole basin scale.Using this method,we evaluated the accuracy of six high-resolution monthly SREs (TRMM 3B42 V6,3B42RT V6,CMORPH,GSMaP MWR+,GSMaP MVK+ and PERSIANN) and revealed the spatio-temporal variation of the SRE accuracy based on spatial interpolated rainfall from a dense network of 325 gauges during 2003-2009 over the Ganjiang River Basin in the Southeast China.The results showed that ground gauge-calibrated 3B42 had the highest accuracy with slight overestimation,whereas the other five uncalibrated SREs had severe underestimation.The accuracy of the six SREs in wet seasons was remarkably higher than that in the dry seasons.When the time scale was expanded,the accuracy of SRE,particularly 3B42,increased.Furthermore,the accuracy of SREs was relatively low in the western mountains and northern piedmont areas,while it was relatively high in the central and southeastern hills and basins of the Ganjiang River Basin.When the space scale was expanded,the accuracy of the six SREs gradually increased.This study provided an example for of SRE accuracy validation in other regions,and a direct basis for further study of SRE-based hydrological process.

A FUSING TECHNIQUE WITH SATELLITE PRECIPITATION ESTIMATE AND RAINGAUGE DATA

Satellite rainfall estimate can provide rainfall information over large areas,and raingauge can provide point-based ground observations with high accuracy.With the combination of satellite and raingauge data together,the estimated rainfall fields are greatly improved.This combination method,called 'fusing technique',is discussed in this paper,and the validation for this technique is accomplished with HUBEX IOP data.

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 usefill 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.

RRVPE:A Robust and Real-Time Visual-Inertial-GNSS Pose Estimator for Aerial Robot Navigation

Self-localization and orientation estimation are the essential capabilities for mobile robot navigation.In this article,a robust and real-time visual-inertial-GNSS(Global Navigation Satellite System)tightly coupled pose estimation(RRVPE)method for aerial robot navigation is presented.The aerial robot carries a front-facing stereo camera for self-localization and an RGB-D camera to generate 3D voxel map.Ulteriorly,a GNSS receiver is used to continuously provide pseudorange,Doppler frequency shift and universal time coordinated(UTC)pulse signals to the pose estimator.The proposed system leverages the Kanade Lucas algorithm to track Shi-Tomasi features in each video frame,and the local factor graph solution process is bounded in a circumscribed container,which can immensely abandon the computational complexity in nonlinear optimization procedure.The proposed robot pose estimator can achieve camera-rate(30 Hz)performance on the aerial robot companion computer.We thoroughly experimented the RRVPE system in both simulated and practical circumstances,and the results demonstrate dramatic advantages over the state-of-the-art robot pose estimators.

Typhoon Hato's precipitation characteristics based on PERSIANN

Heavy precipitation induced by typhoons is the main driver of catastrophic flooding,and studying precipitation patterns is important for flood forecasting and early warning.Studying the space-time characteristics of heavy precipitation induced by typhoons requires a large range of observation data that cannot be obtained by ground-based rain gauge networks.Satellite-based estimation provides large domains of precipitation with high space-time resolution,facilitating the analysis of heavy precipitation patterns induced by typhoons.In this study,Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks(PERSIANN)satellite data were used to study the temporal and spatial features of precipitation induced by Typhoon Hato,which was the strongest typhoon of 2017 to make landfall in China.The results show that rainfall on the land lasted for six days from the typhoon making landfall to disappearing,reaching the maximum when the typhoon made landfall.Hato produced extremely high accumulated rainfall in South China,almost 300 mm in Guangdong Province and Guangxi Zhuang Autonomous Region and 260 mm in Hainan Province.The rainfall process was separated into three stages and rainfall was the focus in the second stage(5 h before making landfall to 35 h after making landfall).