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L-band microwave radiometry is one of the globally accepted techniques to estimate surface soil moisture by measuring upwelling brightness temperature (TB) from the surface. L-band radiometers have been historically used for ground truth validation of space-borne missions. Existing devices are distinguished by using huge and bulky antennas to conduct precise multi-angular measurements at the cost of portability. The current work demonstrates that a portable ground-based L-band microwave radiometer can be used to measure high-resolution TB, soil temperature and emissivity in a case study area in India as an alternative to a traditional huge ground-based radiometer. To show the influence of surface soil moisture (SM) on TB, a controlled experiment was conducted to investigate the temporal variation of TB over a bare soil surface. Spatial analysis of the measured data shows that the compact radiometer can efficiently capture the linear relationship between emissivity and TB, while temporal analysis of TB over a small area shows the variation in TB dynamics with SM. [ABSTRACT FROM AUTHOR]

This letter presents a new approach to incorporate topographic relief effect in land data assimilation system over mountainous terrain. The conventional radiative transfer model (RTM) used for assimilation of microwave brightness temperature (Tb) is subjected to systematic bias owing to its flat earth model assumption. This is theoretically important for direct Tb assimilation system since a difference between simulated and observed Tb may disarray the assimilation system. Here, we consider three crucial relief effects, namely: 1) change in local incidence angle; 2) rotation of the plane of polarization; and 3) effect of pixels in shadow not visible to radiometer. Results indicate that the RTM formulation with relief effects (Topo) significantly reduce the bias as compared to the conventional RTM (Flat). The simulated Tb for the Topo case shows improved sensitivity toward soil moisture as compared to the Flat case. The proposed land surface model (LSM)–RTM assimilation framework has an immense potential source for initialization of LSM for seasonal climate prediction over mountainous terrain. [ABSTRACT FROM AUTHOR]

The spatial error structure of daily precipitation derived from the latest version 7 (v7) tropical rainfall measuring mission (TRMM) level 2 data products are studied through comparison with the Asian precipitation highly resolved observational data integration toward evaluation of the water resources (APHRODITE) data over a subtropical region of the Indian subcontinent for the seasonal rainfall over 6 years from June 2002 to September 2007. The data products examined include v7 data from the TRMM radiometer Microwave Imager (TMI) and radar precipitation radar (PR), namely, 2A12, 2A25, and 2B31 (combined data from PR and TMI). The spatial distribution of uncertainty from these data products were quantified based on performance metrics derived from the contingency table. For the seasonal daily precipitation over a subtropical basin in India, the data product of 2A12 showed greater skill in detecting and quantifying the volume of rainfall when compared with the 2A25 and 2B31 data products. Error characterization using various error models revealed that random errors from multiplicative error models were homoscedastic and that they better represented rainfall estimates from 2A12 algorithm. Error decomposition techniques performed to disentangle systematic and random errors verify that the multiplicative error model representing rainfall from 2A12 algorithm successfully estimated a greater percentage of systematic error than 2A25 or 2B31 algorithms. Results verify that although the radiometer derived 2A12 rainfall data is known to suffer from many sources of uncertainties, spatial analysis over the case study region of India testifies that the 2A12 rainfall estimates are in a very good agreement with the reference estimates for the data period considered. [ABSTRACT FROM PUBLISHER]

Overland rain retrieval using spaceborne microwave radiometer offers a myriad of complications as land presents itself as a radiometrically warm and highly variable background. Hence, land rainfall algorithms of the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) have traditionally incorporated empirical relations of microwave brightness temperature (Tb) with rain rate, rather than relying on physically based radiative transfer modeling of rainfall (as implemented in the TMI ocean algorithm). In this paper, sensitivity analysis is conducted using the Spearman rank correlation coefficient as benchmark, to estimate the best combination of TMI low-frequency channels that are highly sensitive to the near surface rainfall rate from the TRMM Precipitation Radar (PR). Results indicate that the TMI channel combinations not only contain information about rainfall wherein liquid water drops are the dominant hydrometeors but also aid in surface noise reduction over a predominantly vegetative land surface background. Furthermore, the variations of rainfall signature in these channel combinations are not understood properly due to their inherent uncertainties and highly nonlinear relationship with rainfall. Copula theory is a powerful tool to characterize the dependence between complex hydrological variables as well as aid in uncertainty modeling by ensemble generation. Hence, this paper proposes a regional model using Archimedean copulas, to study the dependence of TMI channel combinations with respect to precipitation, over the land regions of Mahanadi basin, India, using version 7 orbital data from the passive and active sensors on board TRMM, namely, TMI and PR. Studies conducted for different rainfall regimes over the study area show the suitability of Clayton and Gumbel copulas for modeling convective and stratiform rainfall types for the majority of the intraseasonal months. Furthermore, large ensembles of TMI Tb (from the most sensitive TMI channel combination) were generated conditional on various quantiles (25th, 50th, 75th, and 95th) of the convective and the stratiform rainfall. Comparatively greater ambiguity was observed to model extreme values of the convective rain type. Finally, the efficiency of the proposed model was tested by comparing the results with traditionally employed linear and quadratic models. Results reveal the superior performance of the proposed copula-based technique. [ABSTRACT FROM PUBLISHER]