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Following the launch of the Global Precipitation Measurement (GPM) Core Observatory, two advanced high resolution multi-satellite precipitation products namely, Integrated Multi-satellitE Retrievals for GPM (IMERG) and Global Satellite Mapping of Precipitation (GSMaP) version 6 are released. A critical evaluation of these newly released precipitation data sets is very important for both the end users and data developers. This study provides a comprehensive assessment of IMERG research product and GSMaP estimates over India at a daily scale for the southwest monsoon season (June to September 2014). The GPM-based precipitation products are inter-compared with widely used TRMM Multisatellite Precipitation Analysis (TMPA), and gauge-based observations over India. Results show that the IMERG estimates represent the mean monsoon rainfall and its variability more realistically than the gauge-adjusted TMPA and GSMaP data. However, GSMaP has relatively smaller root-mean-square error than IMERG and TMPA, especially over the low mean rainfall regimes and along the west coast of India. An entropy-based approach is employed to evaluate the distributions of the selected precipitation products. The results indicate that the distribution of precipitation in IMERG and GSMaP has been improved markedly, especially for low precipitation rates. IMERG shows a clear improvement in missed and false precipitation bias over India. However, all the three satellite-based rainfall estimates show exceptionally smaller correlation coefficient, larger RMSE, larger negative total bias and hit bias over the northeast India where precipitation is dominated by orographic effects. Similarly, the three satellite-based estimates show larger false precipitation over the southeast peninsular India which is a rain-shadow region. The categorical verification confirms that these satellite-based rainfall estimates have difficulties in detection of rain over the southeast peninsula and northeast India. These preliminary results need to be confirmed in other monsoon seasons in future studies when the fully GPM-based IMERG retrospectively processed data prior to 2014 are available.

This study contributes to characterization of satellite precipitation error which is fundamental to develop uncertainty models and bias reduction algorithms. Systematic and random error components of several satellite precipitation products are investigated over different seasons, thresholds and temporal accumulations. The analyses show that the spatial distribution of systematic error has similar patterns for all precipitation products. However, the systematic (random) error of daily accumulations is significantly less (more) than that of high resolution 3‐hr data. One should note that the systematic biases of satellite precipitation are distinctively different in the summer and winter. The systematic (random) error is remarkably higher (lower) during the winter. Furthermore, the systematic error seems to be proportional to the rain rate magnitude. The findings of this study highlight that bias removal methods should take into account the spatiotemporal characteristics of error as well as the proportionality of error to the magnitude of rain rate.

.[1] The components of the Amazon water budget and their spatiotemporal variability are diagnosed using monthly averaged remote sensing-based data products for the period September 2002-December 2006. The large Amazon basin is divided into 14 smaller watersheds, and for each of these sub-basins, fresh water discharge is estimated from the water balance equation using satellite data products. The purpose of this study is to learn how to apply satellite data with global coverage over the large tropical regions; therefore several combinations of remote sensing estimates including total water storage changes, precipitation and evapotranspiration. The results are compared to gauge-based measurements and the best spatiotemporal agreement between estimated and observed runoff is within 1 mm/d for the combination of precipitation from the GPCP and the Montana evapotranspiration product. Mean annual precipitation, evapotranspiration and runoff for the whole basin are estimated to be 6.3, 2.27 and 3.02 mm/d respectively but also show large spatial and temporal variations at sub-basin scale. Using the most consistent data combination, the seasonal dynamics of the water budget within the Amazon system are examined. Agreement between satellite based and in situ runoff is improved when lag-times between sub-basins are included (RMSE from 0.98 to 0.61 mm/d). We estimate these lag times based on satellite-inferred inundation extents. The results reveal not only variations of the basin forcing but also the complex response of the inter-connected sub-basin (SB) water budgets. Inter-annual and inter-SB variation of the water components are investigated and show large anomalies in northwestern and eastern downstream SBs; aggregate behavior of the whole Amazon is more complex than can be represented by a simple integral of the forcing over the whole river system.

This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Reza, "Using microwave brightness temperature diurnal cycle to improve emissivity retrievals over land" (2012 To retrieve microwave land emissivity, infrared surface skin temperatures have been used as surface physical temperature since there is no global information on physical vegetation/soil temperature profiles. However, passive microwave emissions originate from deeper layers with respect to the skin temperature. So, this inconsistency in sensitivity depths between skin temperatures and microwave temperatures may introduce a discrepancy in the determined emissivity. Previous studies showed that this inconsistency can lead to significant differences between day and night retrievals of land emissivity which can exceed 10%. This study proposes an approach to address this inconsistency and improve the retrieval of land emissivity using microwave observations from Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E). The diurnal cycle of the microwave brightness temperature (Tb) was constructed over the globe for different frequencies/polarizations using a constellation of satellites. Principal component analysis (PCA) was conducted to evaluate the spatial variation of the Tb diurnal cycle. The diurnal amplitudes of microwave temperatures observed in desert areas were not consistent with the larger amplitudes of the diurnal cycle of skin temperature. Densely vegetated areas with more moisture have shown smaller amplitudes. A lookup table of effective temperature (T eff ) anomalies is constructed based on the Tb diurnal cycle to resolve the inconsistencies between infrared and Tb diurnal variation. This lookup table of T eff anomalies is a weighted average over the layers contributing to the microwave signal, for each channel and month. The integration of this T eff in the retrieval of land emissivity reduced the differences between day and night retrieved emissivities to less than 0.01 for AMSR-E observations.

Abstract-Uncertainties in the retrievals of microwave land surface emissivities were quantified over two types of land surfaces: desert and tropical rainforest. Retrievals from satellite-based microwave imagers, including SSM/I, TMI and AMSR-E, were studied. Our results show that there are considerable differences between the retrievals from different sensors and from different groups over these two land surface types. In addition, the mean emissivity values show different spectral behavior across the frequencies. With the true emissivity assumed largely constant over both of the two sites throughout the study period, the differences are largely attributed to the systematic and random errors in the retrievals. Generally these retrievals tend to agree better at lower frequencies than at higher ones, with systematic differences ranging 1~4% (3~12 K) over desert and 1~7% (3~20 K) over rainforest. The random errors within each retrieval dataset are in the range of 0.5~2% (2~6 K). In particular, at 85.0/89.0 GHz, there are very large differences between the different retrieval datasets, and within each retrieval dataset itself. Further investigation reveals that these differences are mostly likely caused by rain/cloud contamination, which can lead to random errors up to 10~17 K under the most severe conditions. Index Terms-microwave radiometry, remote sensing, land surface emissivity, measurement uncertainty, systematic errors, random errors, brightness temperature.

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