Gprof

Abstract: This paper describes the latest improvements applied to the Goddard profiling algorithm (GPROF), particularly as they apply to the Tropical Rainfall Measuring Mission (TRMM). Most of these improvements, however, are conceptual in nature and apply equally to other passive microwave sensors. The improvements were motivated by a notable overestimation of precipitation in the intertropical convergence zone. This problem was traced back to the algorithm's poor separation between convective and stratiform precipitation coupled with a poor separation between stratiform and transition regions in the a priori cloud model database. In addition to now using an improved convective–stratiform classification scheme, the new algorithm also makes use of emission and scattering indices instead of individual brightness temperatures. Brightness temperature indices have the advantage of being monotonic functions of rainfall. This, in turn, has allowed the algorithm to better define the uncertainties needed by the sc...

Abstract: Accurate, fine-grained online energy estimation and accounting of mobile devices such as smartphones is of critical importance to understanding and debugging the energy consumption of mobile applications We observe that state-of-the-art, utilization-based power modeling correlates the (actual) utilization of a hardware component with its power state, and hence is insufficient in capturing several power behavior not directly related to the component utilization in modern smartphones Such behavior arise due to various low level power optimizations programmed in the device drivers We propose a new, system-call-based power modeling approach which gracefully encompasses both utilization-based and non-utilization-based power behavior We present the detailed design of such a power modeling scheme and its implementation on Android and Windows Mobile Our experimental results using a diverse set of applications confirm that the new model significantly improves the fine-grained as well as whole-application energy consumption accuracy We further demonstrate fine-grained energy accounting enabled by such a fined-grained power model, via amanually implemented eprof, the energy counterpart of the classic gprof tool, for profiling application energy drain

Abstract: The Goddard profiling algorithm has evolved from a pseudoparametric algorithm used in the current TRMM operational product (GPROF 2010) to a fully parametric approach used operationally in the GPM era (GPROF 2014). The fully parametric approach uses a Bayesian inversion for all surface types. The algorithm thus abandons rainfall screening procedures and instead uses the full brightness temperature vector to obtain the most likely precipitation state. This paper offers a complete description of the GPROF 2010 and GPROF 2014 algorithms and assesses the sensitivity of the algorithm to assumptions related to channel uncertainty as well as ancillary data. Uncertainties in precipitation are generally less than 1%–2% for realistic assumptions in channel uncertainties. Consistency among different radiometers is extremely good over oceans. Consistency over land is also good if the diurnal cycle is accounted for by sampling GMI product only at the time of day that different sensors operate. While accounting...

Abstract: This paper describes a precipitation-retrieval algorithm for the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) that was developed under the Global Satellite Mapping of Precipitation project (GSMaP) by improving the authors' previous algorithm. The basic idea of the GSMaP algorithm is to find the optimal precipitation for which the brightness temperatures (TBs) calculated by the radiative-transfer model (RTM) fit best with the observed TBs. The main improvements of the GSMaP algorithm over the authors' previous work are as follows: (1) use of precipitation-related variable models (precipitation profiles, drop-size distribution, etc.) and precipitation detection and inhomogeneity estimation methods based on TRMM observation studies; (2) use of scattering signals of the TMI Polarization-Corrected Temperature (PCT) at 37 and 85 GHz (PCT37, PCT85) and scattering-signal correction for tall precipitation (thickness between precipitation top level and freezing level (Dtop) larger than 6 km) over land and coastal areas. In order to validate the GSMaP algorithm, we compared its retrievals from TMI TBs in 1998 with the TRMM Precipitation Radar (PR) and Goddard Profiling Algorithm (GPROF) retrievals (2A12 version 6). The results show that (1) over land and coastal areas, the GSMaP retrievals agreed better with PR than GPROF for tall precipitation (Dtop>4 km) weaker than 10 mm h-1, while both GSMaP and GPROF underestimated PR precipitation rates for precipitation heavier than 10 mm h-1; (2) over ocean, the GSMaP retrievals agreed better with PR than GPROF for precipitation heavier than 10 mm h-1, while GSMaP slightly overestimated precipitation weaker than 10 mm h-1 compared to PR and GPROF; (3) The GSMaP algorithm failed to detect some precipitation areas weaker than 2 mm h-1 over sub-tropical oceans. Experimental algorithms with different precipitation-related variable models and retrieval methods using scattering signals were applied to TMI TBs in July 1998 to examine the effect of the above improvements to the GSMaP algorithm. The results show that the improvement of the precipitation profile alleviated the underestimation of precipitation heavier than 10 mm h-1 over land and coastal areas, that the combined use of new physical-related variable models alleviated the underestimation of precipitation heavier than 10 mm h-1 over ocean, and that the use of PCT37 and scattering-signal correction reduced the overestimation of tall precipitation (Dtop>4 km) weaker than 10 mm h-1 over land and coastal areas.