The Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) provides a calibration-based sequential scheme for combining precipitation estimates from multiple satellites, as well as gauge analyses where feasible, at fine scales (0.25° × 0.25° and 3 hourly). TMPA is available both after and in real time, based on calibration by the TRMM Combined Instrument and TRMM Microwave Imager precipitation products, respectively. Only the after-real-time product incorporates gauge data at the present. The dataset covers the latitude band 50°N–S for the period from 1998 to the delayed present. Early validation results are as follows: the TMPA provides reasonable performance at monthly scales, although it is shown to have precipitation rate–dependent low bias due to lack of sensitivity to low precipitation rates over ocean in one of the input products [based on Advanced Microwave Sounding Unit-B (AMSU-B)]. At finer scales the TMPA is successful at approximately reproducing the surface observation–based histogram of precipitation, as well as reasonably detecting large daily events. The TMPA, however, has lower skill in correctly specifying moderate and light event amounts on short time intervals, in common with other finescale estimators. Examples are provided of a flood event and diurnal cycle determination.

A satellite-based rainfall estimation algorithm, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) Cloud Classification System (CCS), is described. This algorithm extracts local and regional cloud features from infrared (10.7 μm) geostationary satellite imagery in estimating finescale (0.04° × 0.04° every 30 min) rainfall distribution. This algorithm processes satellite cloud images into pixel rain rates by 1) separating cloud images into distinctive cloud patches; 2) extracting cloud features, including coldness, geometry, and texture; 3) clustering cloud patches into well-organized subgroups; and 4) calibrating cloud-top temperature and rainfall (Tb–R) relationships for the classified cloud groups using gauge-corrected radar hourly rainfall data. Several cloud-patch categories with unique cloud-patch features and Tb–R curves were identified and explained. Radar and gauge rainfall measurements were both used to evaluate the PERSIANN CCS rainfall estimates at a range of temporal (hourly and daily) and spatial (0.04°, 0.12°, and 0.25°) scales. Hourly evaluation shows that the correlation coefficient (CC) is 0.45 (0.59) at a 0.04° (0.25°) grid scale. The averaged CC of daily rainfall is 0.57 (0.63) for the winter (summer) season.

A systematic inventory of landslide events over the globe is valuable for estimating human and economic losses, quantifying the relationship between landslide occurrences and climate variations and for evaluating emerging global landslide prediction efforts based on remote sensing data. This study compiles a landslide catalog for rainfall-triggered events for several years, drawing upon news reports, scholarly articles, and other hazard databases to provide a landslide catalog at the global scale. While this database may only represent a subset of rainfall-triggered landslides globally, due to lack of reports, it presents a lower boundary on the number of events globally and provides initial insight into the spatiotemporal statistical trends in landslide distribution and impact. This article develops a methodology for landslide event compilation that can be used in evaluating global landslide forecasting initiatives and assessing patterns in landslide distribution and frequency worldwide.

Satellite-based precipitation estimates with high spatial and temporal resolution and large areal coverage provide a potential alternative source of forcing data for hydrological models in regions where conventional in situ precipitation measurements are not readily available. The La Plata basin in South America provides a good example of a case where the use of satellite-derived precipitation could be beneficial. This study evaluates basinwide precipitation estimates from 9 yr (1998–2006) of Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA; 3B42 V.6) through comparison with available gauged data and the Variable Infiltration Capacity (VIC) semidistributed hydrology model applied to the La Plata basin. In general, the TMPA estimates agreed well with the gridded gauge data at monthly time scales, most likely because of the monthly adjustment to gauges performed in TMPA. The agreement between TMPA and gauge precipitation estimates was reduced at daily time scales, particularly for high rain rates. The TMPA-driven hydrologic model simulations were able to capture the daily flooding events and to represent low flows, although peak flows tended to be biased upward. There was a good agreement between TMPA-driven simulated flows in terms of their reproduction of seasonal and interannual streamflow variability. This analysis shows that TMPA has potential for hydrologic forecasting in data-sparse regions.