Abstract

Previous observational studies indicated that local sea surface temperatures (SSTs) near the west coast of the United States, in the Gulf of California, and in the Gulf of Mexico have strong impacts on the North American monsoon (NAM) system. Simulations of the NAM by numerical models are also found to be sensitive to the specification of SSTs. Accordingly, a reliable SST dataset is essential for improving the understanding, simulation, and prediction of the NAM system. In this study, a new fine-resolution SST analysis is constructed by merging in situ observations from ships and buoys with retrievals from National Oceanic and Atmospheric Administration (NOAA) satellites (NOAA-16 and NOAA-17), Geostationary Operational Environmental Satellites (GOES), the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), and the Advanced Microwave Scanning Radiometer (AMSR). Called the multiplatform-merged (MPM) SST analysis, this new product of 3-hourly SST is defined on a 0.25° × 0.25° latitude–longitude grid over the Western Hemisphere (30°S–60°N, 180°–30°W). The analysis for the period of 15 May–30 September 2004 shows that the MPM is capable of capturing small-scale disturbances such as those associated with the tropical instability waves. It also depicts local sharp gradients around Baja California and the Gulf Stream with reasonable accuracy compared with the existing analyses. Experiments have been conducted to examine the impacts of the addition of satellite observations on the quality of the MPM analysis. Results showed that inclusion of observations from more satellites progressively improves the quantitative accuracy, especially for diurnal amplitude of the analysis, indicating the importance of accommodating observations from multiple platforms in depicting critical details in an SST analysis with high temporal and spatial resolutions.

Abstract

In order to further our quantitative understanding of the advantages and the shortcomings of the various sources of data used to represent climatic-scale precipitation, monthly gauge observations and satellite estimates are intercompared for global grid areas of 2.5° latitude/longitude for a period from July 1987 to June 1990. The results show that 1) at least five gauges are necessary to construct an areal-averaged monthly mean for the grids with accuracy of 10%, and 10% of the global land grids satisfy the requirement; 2) both microwave- and IR-based satellite estimates give similar spatial distributions of precipitation with good agreement with gauge observations for the warm seasons and over the tropical Pacific Ocean; and 3) the satellite estimates, especially those from the IR-based algorithm, exhibit poorer correspondence with gauge observations over land areas for the cold seasons. These results show that, for many applications, no single type of data can be used as the source for a monthly precipitation dataset with full global coverage, suggesting the need to improve the algorithms and to develop methods of combining the individual data sources, particularly in estimating extratropical precipitation.

Abstract

An algorithm is developed to construct global gridded fields of monthly precipitation by merging estimates from five sources of information with different characteristics, including gauge-based monthly analyses from the Global Precipitation Climatology Centre, three types of satellite estimates [the infrared-based GOES Precipitation Index, the microwave (MW) scattering-based Grody, and the MW emission-based Chang estimates], and predictions produced by the operational forecast model of the European Centre for Medium-Range Weather Forecasts. A two-step strategy is used to: 1) reduce the random error found in the individual sources and 2) reduce the bias of the combined analysis. First, the three satellite-based estimates and the model predictions are combined linearly based on a maximum likelihood estimate, in which the weighting coefficients are inversely proportional to the squares of the individual random errors determined by comparison with gauge observations and subjective assumptions. This combined analysis is then blended with an analysis based on gauge observations using a method that presumes that the bias of the gauge-based field is small where sufficient gauges are available and that the gradient of the precipitation field is best represented by the combination of satellite estimates and model predictions elsewhere. The algorithm is applied to produce monthly precipitation analyses for an 18-month period from July 1987 to December 1988. Results showed substantial improvements of the merged analysis relative to the individual sources in describing the global precipitation field. The large-scale spatial patterns, both in the Tropics and the extratropics, are well represented with reasonable amplitudes. Both the random error and the bias have been reduced compared to the individual data sources, and the merged analysis appears to be of reasonable quality everywhere. However, the actual quality of the merged analysis depends strongly on our uncertain and incomplete knowledge of the error structures of the individual data sources.

The Global Precipitation Climatology Project (GPCP) was established by the World Climate Research Programme to produce global analyses of area- and time-averaged precipitation for use in climate research. To achieve the required spatial coverage, the GPCP uses simple rainfall estimates derived from IR and microwave satellite observations. In this paper, we describe the GPCP and its first Algorithm Intercomparison Project (AIP/1), which compared a variety of rainfall estimates derived from Geostationary Meteorological Satellite visible and IR observations and Special Sensor Microwave/Imager microwave observations with rainfall derived from a combination of radar and raingage data over the Japanese islands and the adjacent ocean regions during the June and mid-July through mid-August periods of 1989. To investigate potential improvements in the use of satellite IR data for the estimation of large-scale rainfall for the GPCP, the relationship between rainfall and the fractional coverage of cold clouds in the AIP/1 dataset is examined. Linear regressions between fractional coverage and rainfall are analyzed for a number of latitude-longitude areas and for a range of averaging times. The results show distinct differences in the character of the relationship for different portions of the area. In general, to the south and east of the mountainous axis of Japan, rainfall and fractional coverage are highly correlated for thresholds colder than 245 K, and correlations can be increased by averaging in space and in time up to the dominant period of the precipitation events. To the north and west of the axis, the correlations between rainfall and fractional coverage, while generally smaller for all scales, are highest for thresholds warmer than 245 K. The proportional coefficients relating rainfall to fractional coverage at cold thresholds, however, differ greatly between the two periods and both differ significantly from those found for the GARP (Global Atmospheric Research Program) AtlanticTropical Experiment. These results suggest that the simple IR-based estimation technique currently used in the GPCP can be used to estimate rainfall for global tropical and subtropical areas, provided that a method for adjusting the proportional coefficient for varying areas and seasons can be determined.

Gridded fields (analyses) of global monthly precipitation have been constructed on a 2.5° latitude–longitude grid for the 17-yr period from 1979 to 1995 by merging several kinds of information sources with different characteristics, including gauge observations, estimates inferred from a variety of satellite observations, and the NCEP–NCAR reanalysis. This new dataset, which the authors have named the CPC Merged Analysis of Precipitation (CMAP), contains precipitation distributions with full global coverage and improved quality compared to the individual data sources. Examinations showed no discontinuity during the 17-yr period, despite the different data sources used for the different subperiods. Comparisons of the CMAP with the merged analysis of Huffman et al. revealed remarkable agreements over the global land areas and over tropical and subtropical oceanic areas, with differences observed over extratropical oceanic areas. The 17-yr CMAP dataset is used to investigate the annual and interannual variability in large-scale precipitation. The mean distribution and the annual cycle in the 17-yr dataset exhibit reasonable agreement with existing long-term means except over the eastern tropical Pacific. The interannual variability associated with the El Niño-Southern Oscillation phenomenon resembles that found in previous studies, but with substantial additional details, particularly over the oceans. With complete global coverage, extended period and improved quality, the 17-yr dataset of the CMAP provides very useful information for climate analysis, numerical model validation, hydrological research, and many other applications. Further work is under way to improve the quality, extend the temporal coverage, and to refine the resolution of the merged analysis.

Abstract

The relationship between the flux of outgoing longwave radiation (OLR) estimated from satellite observations and precipitation is investigated using monthly OLR data from the NOAA polar-orbiting satellites and the merged analysis of precipitation of Xie and Arkin for the 8-yr period from July 1987 to June 1995. The mean annual cycle of OLR in the Tropics is dominated by changes in cloudiness and exhibits a strong negative correlation with precipitation, while in the extratropics the strongest influence on the annual cycle of OLR is surface temperature and a positive correlation with precipitation is found. However, the anomaly of OLR exhibits a negative correlation with precipitation over most of the globe. The regression coefficient relating the anomaly of precipitation to that of OLR is spatially inhomogeneous and seasonally dependent but can be expressed with high accuracy as a globally uniform linear function of the local mean precipitation. Based on these results, a new technique is developed to estimate monthly precipitation over the globe from OLR data. First, the mean annual cycle of precipitation is calculated from the merged analysis of precipitation for the 8-yr period. The precipitation anomaly is then estimated from the OLR anomaly field using the coefficient value appropriate for the mean annual cycle of precipitation at each location. Finally, the total precipitation is estimated as the sum of the mean annual cycle and the anomaly. Verification tests showed that this estimate, which is referred to here as the OLR-based precipitation index (OPI), is able to represent large-scale precipitation with globally uniform and temporally stable high quality, similar to geostationary satellite IR-based estimates over the Tropics and to estimates based on microwave scattering observations over extratropical areas. The OPI estimates are then produced for the 22-yr period from 1974 to 1995 and are used to investigate the annual and interannual variability of global precipitation. The mean distribution and seasonal variations as observed in the 22-yr set of OPI estimates agree well with those of several published long-term means of precipitation estimated from station observations, and the interannual variability in precipitation associated with the El Niño–Southern Oscillation phenomenon resemble those found in previous studies but with additional details, particularly over ocean areas.

Abstract

A method has been developed to produce real-time rain gauge–satellite merged analyses of global monthly precipitation. A dataset of these analyses spans the period from January 1979 to the present, which is sufficiently long to allow the computation of reasonably stable base period means from which departures from “normal” can be computed. The dataset is used routinely for global precipitation monitoring purposes at the National Oceanic and Atmospheric Administration/National Weather Service/National Centers for Environmental Prediction/Climate Prediction Center, is updated monthly, and is available via the Internet.

Abstract

A pentad version of the Global Precipitation Climatology Project global precipitation dataset is used to document the annual and interannual variations in precipitation over monsoon regions around the globe. An algorithm is described that determines objectively wet season onset and withdrawal for individual years, and this tool is used to examine the behavior of various characteristics of the major monsoon systems. The definition of onset and withdrawal are determined by examining the ramp-up and diminution of rainfall within the context of the climatological rainfall at each location. Also examined are interannual variations in onset and withdrawal and their relationship to rainy season precipitation accumulations. Changes in the distribution of “heavy” and “light” precipitation events are examined for years in which “abundant” and “poor” wet seasons are observed, and associations with variations in large-scale atmospheric general circulation features are also examined. In particular, some regions of the world have strong associations between wet season rainfall and global-scale patterns of 200-hPa streamfunction anomalies.

Abstract

A Kalman filter (KF)-based Climate Prediction Center (CPC) morphing technique (CMORPH) algorithm is developed to integrate the passive microwave (PMW) precipitation estimates from low-Earth-orbit (LEO) satellites and infrared (IR) observations from geostationary (GEO) platforms. With the new algorithm, the precipitation analysis at a grid box of 8 × 8 km² is defined in three steps. First, PMW estimates of instantaneous rain rates closest to the target analysis time in both the forward and backward directions are propagated from their observation times to the analysis time using the cloud system advection vectors (CSAVs) computed from the GEO–IR images. The “prediction” of the precipitation analysis is then defined by averaging the forward- and backward-propagated PMW estimates with weights inversely proportional to their error variance. The IR-based precipitation estimates are incorporated if the gap between the two PMW observations is longer than 90 min. Validation tests showed substantial improvements of the KF-based CMORPH against the original version in both the pattern correlation and fidelity of probability density function (PDF) of the precipitation intensity. In general, performance of the original CMORPH degrades sharply with poor pattern correlation and substantially elevated (damped) frequency for light (heavy) precipitation events when PMW precipitation estimates are available from fewer LEO satellites. The KF-based CMORPH is capable of producing high-resolution precipitation analysis with much more stable performance with various levels of availability for the PMW observations.

Abstract

This paper describes the initial work toward the production of monthly global (land and ocean) analyses of precipitation for an extended period from 1948 to the present. Called the precipitation reconstruction (PREC), the global analyses are defined by interpolation of gauge observations over land (PREC/L) and by EOF reconstruction of historical observations over ocean (PREC/O). This paper documents the creation of the land component of the analyses (PREC/L) on a 2.5° latitude/longitude grid for 1948–2000. These analyses are derived from gauge observations from over 17 000 stations collected in the Global Historical Climatology Network (GHCN), version 2, and the Climate Anomaly Monitoring System (CAMS) datasets. To determine the most suitable objective analysis procedure for gridding, the analyses generated by four published objective analysis techniques [those of Cressman, Barnes, and Shepard, and the optimal interpolation (OI) method of Gandin] were compared. The evaluation demonstrated two crucial points: 1) better results are obtained when interpolating anomalies rather than the precipitation totals, and 2) the OI analysis procedure provided the most accurate and stable analyses among the four algorithms that were tested. Based on these results, the OI technique was used to create monthly gridded analyses of precipitation over the global land areas for the 53-yr period from 1948 to 2000. In addition, some diagnostic investigations of the seasonal and interannual variability of large-scale precipitation over the global land areas are presented. The mean distribution and annual cycle of precipitation observed in the PREC/L showed good agreement with those in several published gauge-based datasets, and the anomaly patterns associated with ENSO resemble those found in previous studies. The gauge-based dataset (PREC/L) will be updated on a quasi-real-time basis and is available online (ftp.ncep.noaa.gov/pub/precip/50-yr).