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ABSTRACT Precipitation is a key source of freshwater; therefore, observing global patterns of precipitation and its intensity is important for science, society, and understanding our planet in a changing climate. [...]

Precipitation is the fundamental source of freshwater in the water cycle. It is critical for everyone, from subsistence farmers in Africa to weather forecasters around the world, to know when, [...]

The measurement of global precipitation, both rainfall and snowfall, is critical to a wide range of users and applications. Rain gauges are indispensable in the measurement of precipitation, remaining the [...]

The measurement of global precipitation, both rainfall and snowfall, is critical to a wide range of users and applications. Rain gauges are indispensable in the measurement of precipitation, remaining the [...]

ABSTRACT Understanding observed changes to the global water cycle is key to predicting future climate changes and their impacts. While many datasets document crucial variables such as precipitation, ocean salinity, [...]

Accurate estimation of high-resolution precipitation on the global scale is extremely challenging. The operational Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) has created over 16 years of high-resolution [...]

Monthly and daily products of the Global Precipitation Climatology Project (GPCP) are evaluated through a comparison with Finnish Meteorological Institute (FMI) gauge observations for the period January 1995-December 2007 to assess the quality of the GPCP estimates at high latitudes. At the monthly scale both the final GPCP combination satellite--gauge (SG) product is evaluated, along with the satellite-only multisatellite (MS) product. The GPCP daily product is scaled to sum to the monthly product, so it implicitly contains monthly-scale gauge influence, although it contains no daily gauge information. As expected, the monthly SG product agrees well with the FMI observations because of the inclusion of limited gauge information. Over the entire analysis period the SG estimates are biased low by 6% when the same wind-loss adjustment is applied to the FMI gauges as is used in the SG analysis. The interannual anomaly correlation is about 0.9. The satellite-only MS product has a lesser, but still reasonably good, interannual correlation (~0.6) while retaining a similar bias due to the use of a climatological bias adjustment. These results indicate the value of using even a few gauges in the analysis and provide an estimate of the correlation error to be expected in the SG analysis over ocean and remote land areas where gauges are absent. The daily GPCP precipitation estimates compare reasonably well at the 1[degrees] latitude x 2[degrees] longitude scale with the FMI gauge observations in the summer with a correlation of 0.55, but less so in the winter with a correlation of 0.45. Correlations increase somewhat when larger areas and multiday periods are analyzed. The day-to-day occurrence of precipitation is captured fairly well by the GPCP estimates, but the corresponding precipitation event amounts tend to show wide variability. The results of this study indicate that the GPCP monthly and daily fields are useful for meteorological and hydrological studies but that there is significant room for improvement of satellite retrievals and analysis techniques in this region. It is hoped that the research here provides a framework for future high-latitude assessment efforts such as those that will be necessary for the upcoming satellite-based Global Precipitation Measurement (GPM) mission.

Landslides triggered by rainfall can possibly be foreseen in real time by jointly using rainfall intensity-duration thresholds and information related to land surface susceptibility. However, no system exists at either a national or a global scale to monitor or detect rainfall conditions that may trigger landslides due to the lack of sufficient ground-based observing network in many parts of the world. Recent advances in satellite remote sensing technology and increasing availability of high-resolution geospatial products around the globe have provided an unprecedented opportunity for such a study. In this paper, a framework for developing an experimental real-time prediction system to identify where rainfall-triggered landslides will occur is proposed by combining two necessary components: surface landslide susceptibility (LS) and a real-time space-based rainfall analysis system. First, a global LS map is derived from a combination of semistatic global surface characteristics (digital elevation topography, slope, soil types, soil texture, land cover classification, etc.) using a geographic information system weighted linear combination approach. Second, an adjusted empirical relationship between rainfall intensity-duration and landslide occurrence is used to assess landslide hazards at areas with high susceptibility. A major outcome of this paper is the availability for the first time of a global assessment of landslide hazards, which is only possible because of the utilization of global satellite remote sensing products. This experimental system can be updated continuously using the new satellite remote sensing products. This proposed system, if pursued through wide interdisciplinary efforts as recommended herein, bears the promise to grow many local landslide hazard analyses into a global decision-making support system for landslide disaster preparedness and mitigation activities across the world. Index Terms--Landslide, landslide susceptibility (LS), natural disasters, real-time precipitation analysis, satellite remote sensing.

This study provides an intensive evaluation of the Global Precipitation Climatology Project (GPCP) 1[degrees] daily (1DD) rainfall products over the Mississippi River basin, which covers 435 1[degrees] latitude x 1[degrees] longitude grids for the period of January 1997-December 2000 using radar-based precipitation estimates. The authors' evaluation criteria include unconditional continuous, conditional (quasi) continuous, and categorical statistics, and their analyses cover annual and seasonal time periods. The authors present spatial maps that reflect the results for the 1 [degrees] grids and a summary of the results for three selected regions. They also develop a statistical framework that partitions the GPCP--radar difference statistics into GPCP error and radar error statistics. They further partition the GPCP error statistics into sampling error and retrieval error statistics and estimate the sampling error statistics using a data-based resampling experiment. Highlights of the results include the following: 1) the GPCP 1DD product captures the spatial and temporal variability of rainfall to a high degree, with more than 80% of the variance explained, 2) the GPCP 1DD product proficiently detects rainy days at a large range of rainfall thresholds, and 3) in comparison with radar-based estimates the GPCP 1DD product overestimates rainfall.

This paper focuses on estimating the error uncertainty of the monthly 2.5[degrees] x 2.5[degrees] rainfall products of the Global Precipitation Climatology Project (GPCP) using rain gauge observations. Two kinds of GPCP products are evaluated: the satellite-only (MS) product, and the satellite--gauge (SG) merged product. The error variance separation (EVS) method has been proposed previously as a means of estimating the error uncertainty of the GPCP products. In this paper, the accuracy of the EVS results is examined for a variety of gauge densities. Three validation sites--two in North Dakota and one in Thailand--all with a large number of rain gauges, were selected. The very high density of the selected sites justifies the assumption that the errors are negligible if all gauges are used. Monte Carlo simulation studies were performed to evaluate sampling uncertainty for selected rain gauge network densities. Results are presented in terms of EVS error uncertainty normalized by the true error uncertainty. These results show that the accuracy of the EVS error uncertainty estimates for the SG product differs from that of the MS product. The key factors that affect the errors of the EVS results, such as the gauge density, the gauge network, and the sample size, have been identified and their influence has been quantified. One major finding of this study is that 8-10 gauges, at the 2.5[degrees] scale, are required as a minimum to get good error uncertainty estimates for the SG products from the EVS method. For eight or more gauges, the normalized error uncertainty is about 0.86 [+ or -] 0.10 (North Dakota: box 1) and 0.95 [+ or -] 0.10 (North Dakota: box 2). Results show that, despite its error, the EVS method performs better than the root-mean-square error (rmse) approach that ignores the rain gauge sampling error. For the MS products, both the EVS method and the rmse approach give negligible bias. As expected, results show that the SG products give better rainfall estimates than the MS products, according to most of the criteria used.

The success of any passive microwave precipitation retrieval algorithm relies on the proper identification of rain areas and the elimination of surface areas that produce a signature similar to that of precipitation. A discussion on the impact of and on methods that identify areas of rain, snow cover, deserts, and semiarid conditions over land, and rain, sea ice, strong surface winds, and clear, calm conditions over ocean. are presented. Additional artifacts caused by coastlines and Special Sensor Microwave/Imager data errors are also discussed, and methods to alleviate their impact are presented. The strengths and weaknesses of the 'screening' techniques are examined through application on various case studies used in the WetNet PIP-2. Finally, a methodology to develop a set of screens for use as a common rainfall indicator for the intercomparison of the wide variety of algorithms submitted to PIP-2 is described.

The random errors contained in a finite set E of precipitation estimates result from both finite sampling and measurement-algorithm effects. The expected root-mean-square random error associated with the estimated average precipitation in E is shown to be [Mathematical Expression Omitted], where [Mathematical Expression Omitted] is the space-time-average precipitation estimate over E, H is a function of the shape of the probability distribution of precipitation (the nondimensional second moment), p is the frequency of nonzero precipitation in E, and [N.sub.I] is the number of independent samples in E. All of these quantities are variables of the space-time-average dataset. In practice H is nearly constant and close to the value 1.5 over most of the globe. An approximate form of [[Sigma].sub.r] is derived that accommodates the limitations of typical monthly datasets, then it is applied to the microwave, infrared, and gauge precipitation monthly datasets from the Global Precipitation Climatology Project. As an aid to visualizing differences in [[Sigma].sub.r] for various datasets, a 'quality index' is introduced. Calibration in a few locations with dense gauge networks reveals that the approximate form is a reasonable first step in estimating [[Sigma].sub.r].

The Global Precipitation Climatology Project (GPCP) has released the GPCP Version 1 Combined Precipitation Data Set, a global, monthly precipitation dataset covering the period July 1987 through December 1995. The primary product in the dataset is a merged analysis incorporating precipitation estimates from low-orbit-satellite microwave data, geosynchronous-orbit-satellite infrared data, and rain gauge observations. The dataset also contains the individual input fields, a combination of the microwave and infrared satellite estimates, and error estimates for each field. The data are provided on 2.5 [degrees] x 2.5 [degrees] latitude-longitude global grids. Preliminary analyses show general agreement with prior studies of global precipitation and extends prior studies of El Nino - Southern Oscillation precipitation patterns. At the regional scale there are systematic differences with standard climatologies.

Climatologies of convective precipitation were derived from passive microwave observations from the Special Sensor Microwave Imager using a scattering-based algorithm of Adler et al. Data were aggregated over periods of 3-5 months using data from 4 to 5 years. Data were also stratified by satellite overpass times (primarily 06 00 and 18 00 local time). Four regions [Mexico, Amazonia, western Africa, and the western equatorial Pacific Ocean (TOGA COARE area)] were chosen for their meteorological interest and relative paucity of conventional observations. The strong diurnal variation over Mexico and the southern United States was the most striking aspect of the climatologies. Pronounced morning maxima occurred offshore, often in concavities in the coastline, the result of the increased convergence caused by the coastline shape. The major feature of the evening rain field was a linear-shaped maximum along the western slope of the Sierra Madre Occidental. Topography exerted a strong control on the rainfall in other areas, particularly near the Nicaragua/Honduras border and in Guatemala, where maxima in excess of 700 mm [month.sup.-1] were located adjacent to local maxima in terrain. The correlation between the estimates and monthly gauge data over the southern United States was low (0.45), due mainly to poor temporal sampling in any month and an inadequate sampling of the diurnal cycle. Over the Amazon Basin the differences in morning versus evening rainfall were complex, with an alternating series of morning/evening maxima aligned southwest to northeast from the Andes to the northeast Brazilian coast. Areal extent of rainfall in Amazonia was slightly higher in the evening, but a maximum in morning precipitation was found on the Amazon River just east of Manaus. Precipitation over the water in the ITCZ north of Brazil was more pronounced in the morning, and a pronounced land-/sea-breeze circulation was found along the northeast coast of Brazil. Inter-comparison of four years revealed 1992 to be the driest over Amazonia, with about a 23% decrease in mean rain rate compared to the 4-year mean estimated rain rate. The major rain feature of tropical western Africa was found on the west coast - a pronounced overland evening maximum directly between the coast and a high mountain peak, and a morning maximum directly offshore. An intense, localized morning maximum of over 1000 mm [month.sup.-1] was found at a concavity in the coast at the Bight of Bonny. In the region of the TOGA COARE experiment, precipitation in the ITCZ was greater in November 1989-February 1990, compared to the same period in 1988-1989, notably in the region five degrees either side of the equator from 160 [degrees] E to the dateline. There was a clear preference in both seasons for morning precipitation over the water. Interesting diurnal effects were found over and offshore of New Guinea and the Solomon Islands. For the eight months studied, averaging both the gauges and Goddard Scattering Algorithm estimates to 2.5 [degrees] grid boxes yielded a correlation of 0.73, bias of -59.5 mm, and a root-mean-square difference of 131.8 mm - 29% and 64%, respectively, of the mean monthly observed rainfall.

The equivalent brightness temperature [T.sub.b] recorded by geosynchronous infrared (geo-IR) 'window' channel (10.7-11.5 [micro]m) satellite sensors is shown to depend on the zenith angle (local angle from the zenith to the satellite for a pixel's ground location) in addition to the mix of clouds and surface that would be observed from a direct overhead viewpoint (nadir view). This zenith-angle dependence is characterized, and two corrections are developed from a collection of half-hourly geo-IR pixel data that have been parallax corrected and averaged to a 0.5 [degrees] X 0.5 [degrees] latitude/longitude grid for each geosynchronous satellite separately. First, composites of collocated [T.sub.b] over tropical regions from the Geostationary Operational Environmental Satellite (GOES)-8/GOES-10 and the Meteosat-5/Meteosat-7 satellite pairs are used to produce robust estimates of isotropic zenith-angle corrections as a function of zenith angle and grid-box-averaged Th. The corrections range from zero for a zenith angle of ~26.5 [degrees] to increases of more than 20 K at [T.sub.b] = 230 K near the limb. Near-limb corrections in clear and very cold thick overcast conditions are smaller but are still positive. This empirical result depends on the surface--tropopause temperature differences, so a second correction was developed. Using collocations from the same two satellite pairs from 60 [degrees] N to 60 [degrees] S, differences in uncorrected [T.sub.b] divided by differences in the corresponding corrected values were accumulated as a function of latitude and season. The resultant ratios smoothly vary from ~1 in the Tropics to ~0.5 at 60 [degrees] N and 60 [degrees] S, with a quicker decrease in the winter hemisphere. In comparison with the uncorrected geo-IR data, there is a 50% reduction in the root-mean-square differences between collocated values from adjacent satellites by applying the latitude/season-adjusted zenith-angle corrections. Histograms of corrected geo-IR [T.sub.b] at large zenith angles closely match histograms from collocated near-nadir-view values. Residual difference maps are smooth and indicate intersatellite differences.

A technique is described to use Tropical Rainfall Measuring Mission (TRMM) combined radar--radiometer information to adjust geosynchronous infrared satellite data [the TRMM AdJusted Geostationary Operational Environmental Satellite Precipitation Index (AGPI)]. The AGPI is then merged with rain gauge information (mostly over land) to provide finescale (1 [degrees] latitude x 1 [degrees] longitude) pentad and monthly analyses, respectively. The TRMM merged estimates are 10% higher than those from the Global Precipitation Climatology Project (GPCP) when integrated over the tropical oceans (37 [degrees] N-37 [degrees] S) for 1998, with 20% differences noted in the most heavily raining areas. In the dry subtropics the TRMM values are smaller than the GPCP estimates. The TRMM merged product tropical-mean estimates for 1998 are 3.3 mm [day.sup.-1] over ocean and 3.1 mm [day.sup.-1] over land and ocean combined. Regional differences are noted between the western and eastern Pacific Ocean maxima when TRMM and GPCP are compared. In the eastern Pacific rain maximum the TRMM and GPCP mean values are nearly equal, which is very different from the other tropical rainy areas where TRMM merged product estimates are higher. This regional difference may indicate that TRMM is better at taking into account the vertical structure of the rain systems and the difference in structure between the western and eastern (shallower) Pacific convection. Comparisons of these TRMM merged analysis estimates with surface datasets shows varied results; the bias is near zero when compared with western Pacific Ocean atoll rain gauge data, but is significantly positive as compared with Kwajalein radar estimates (adjusted by rain gauges). Over land the TRMM estimates also show a significant positive bias. The inclusion of gauge information in the final merged product significantly reduces the bias over land, as expected. The monthly precipitation patterns produced by the TRMM merged data process clearly show the evolution of the El Nino-Southern Oscillation (ENSO) tropical precipitation pattern from early 1998 (El Nino) to early 1999 (La Nina) and beyond. The El Nino-minus-La Nina difference map shows the expected eastern Pacific maximum, the 'Maritime Continent' minima, and other tropical and midlatitude features, very similar to those detected by the GPCP analyses. However, summing the El Nino-minus-La Nina differences over the global tropical oceans yields divergent answers for interannual changes from TRMM, GPCP, and other estimates. This emphasizes the need for additional validation and analysis before it is feasible to understand the relations between global precipitation anomalies and Pacific Ocean ENSO temperature changes.