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 scheme's Bayesian inversion approach. Last, the algorithm over land has been modified primarily to better account for ambiguous classification where the scattering signature of precipitation could be confused with surface signals. All these changes have been implemented for both the TRMM Microwave Imager (TMI) and the Special Sensor Microwave Imager (SSM/I). Results from both sensors are very similar at the storm scale and for global averages. Surface rainfall products from the algorithm's operational version have been compared with conventional rainfall data over both land and oceans. Over oceans, GPROF results compare well with atoll gauge data. GPROF is biased negatively by 9% with a correlation of 0.86 for monthly 2.5Њ averages over the atolls. If only grid boxes with two or more atolls are used, the correlation increases to 0.91 but GPROF becomes positively biased by 6%. Comparisons with TRMM ground validation products from Kwajalein reveal that GPROF is negatively biased by 32%, with a correlation of 0.95 when coincident images of the TMI and Kwajalein radar are used. The absolute magnitude of rainfall measured from the Kwajalein radar, however, remains uncertain, and GPROF overestimates the rainfall by approximately 18% when compared with estimates done by a different research group. Over land, GPROF shows a positive bias of 17% and a correlation of 0.80 over monthly 5Њ grids when compared with the Global Precipitation Climatology Center (GPCC) gauge network. When compared with the precipitation radar (PR) over land, GPROF also retrieves higher rainfall amounts (20%). No vertical hydrometeor profile information is available over land. The correlation with the TRMM precipitation radar is 0.92 over monthly 5Њ grids, but GPROF is positively biased by 24% relative to the radar over oceans. Differences between TMI-and PR-derived vertical hydrometeor profiles below 2 km are consistent with this bias but become more significant with altitude. Above 8 km, the sensors disagree significantly, but the information content is low...

Abstract:The new Version 2.3 of the Global Precipitation Climatology Project (GPCP) Monthly analysis is described in terms of changes made to improve the homogeneity of the product, especially after 2002. These changes include corrections to cross-calibration of satellite data inputs and updates to the gauge analysis. Over-ocean changes starting in 2003 resulted in an overall precipitation increase of 1.8% after 2009. Updating the gauge analysis to its final, high-quality version increases the global land total by 1.8% for the post-2002 period. These changes correct a small, incorrect dip in the estimated global precipitation over the last decade given by the earlier Version 2.2. The GPCP analysis is also used to describe global precipitation in 2017. The general La Niña pattern for 2017 is noted and the evolution from the early 2016 El Niño pattern is described. The 2017 global value is one of the highest for the 1979-2017 period, exceeded only by 2016 and 1998 (both El Niño years), and reinforces the small positive trend. Results for 2017 also reinforce significant trends in precipitation intensity (on a monthly scale) in the tropics. These results for 2017 indicate the value of the GPCP analysis, in addition to research, for climate monitoring.

No abstract

No abstract

Approximately 20% of couples worldwide are infertile and about half of these couples have male infertility. Therefore, it is important to develop effective strategies for preventing male infertility. In this study, we examined the effects and regulatory mechanisms of curcumin, an active ingredient in the traditional herbal treatment derived from the dietary spice turmeric (Curcuma longa), on exogenous scrotal heat stress-induced testicular injuries in mice. Adult mice were orally administered three different doses of curcumin (20, 40, or 80 mg per kg per day) for 14 consecutive days and then subjected to transient scrotal heat stress at 43 °C for 20 min on day 7. The testes and blood of the mice were collected on day 14. Mice exposed to heat stress showed low testicular weight, severe vacuolization of seminiferous tubules followed by loss of spermatogenic cells, and the appearance of multinucleated giant cells and degenerative Leydig cells. In addition, great changes in oxidative stress (lipid peroxidation, superoxide dismutase (SOD) activity, cytoplasmic SOD, mitochondrial SOD, and phospholipid hydroperoxide glutathione peroxidase mRNAs), apoptosis (B-cell lymphoma-extra large and caspase 3 mRNAs), heat shock reaction (heat shock transcription factor-1 and transforming growth factor-β1 mRNAs) and androgen biosynthesis (testosterone concentration and 3β-hydroxysteroid dehydrogenase mRNA) were observed. However, all these testicular injuries induced by the scrotal hyperthermia were significantly improved by curcumin treatment (20, 40 and 80 mg kg(-1)) in a dose-dependent manner via its antioxidative, anti-apoptotic and androgen synthesis effects, indicating that it has the potential to prevent male infertility.