The Global Precipitation Climatology Project (GPCP) Version-2 Monthly Precipitation Analysis is described. This globally complete, monthly analysis of surface precipitation at 2.5 latitude 2.5 longitude resolution is available from January 1979 to the present. It is a merged analysis that incorporates precipitation estimates from low-orbit satellite microwave data, geosynchronous-orbit satellite infrared data, and surface rain gauge observations. The merging approach utilizes the higher accuracy of the low-orbit microwave observations to calibrate, or adjust, the more frequent geosynchronous infrared observations. The dataset is extended back into the prem-icrowave era (before mid-1987) by using infrared-only observations calibrated to the microwave-based analysis of the later years. The combined satellite-based product is adjusted by the rain gauge analysis. The dataset archive also contains the individual input fields, a combined satellite estimate, and error estimates for each field. This monthly analysis is the foundation for the GPCP suite of products, including those at finer temporal resolution. The 23-yr GPCP climatology is characterized, along with time and space variations of precipitation.

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° x2.5° 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.

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.

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 for only a modest amount of the total precipitation, snow-covered surfaces exhibit differences of up to 25% between sensors traceable to the availability of high-frequency (166 and 183 GHz) channels. In general, comparisons against early versions of GPM’s Ku-band radar precipitation estimates are fairly consistent but absolute differences will be more carefully evaluated once GPROF 2014 is upgraded to use the full GPM-combined radar–radiometer product for its a priori database. The combined algorithm represents a physically constructed database that is consistent with both the GPM radars and the GMI observations, and thus it is the ideal basis for a Bayesian approach that can be extended to an arbitrary passive microwave sensor.

As part of the Global Precipitation Climatology Project (GPCP), analyses of pentad precipitation have been constructed on a 2.5Њ latitude-longitude grid over the globe for a 23-yr period from 1979 to 2001 by adjusting the pentad Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP) against the monthly GPCP-merged analyses. This adjustment is essential because the precipitation magnitude in the pentad CMAP is not consistent with that in the monthly CMAP or monthly GPCP datasets primarily due to the differences in the input data sources and merging algorithms, causing problems in applications where joint use of the pentad and monthly datasets is necessary. First, pentad CMAP-merged analyses are created by merging several kinds of individual data sources including gauge-based analyses of pentad precipitation, and estimates inferred from satellite observations. The pentad CMAP dataset is then adjusted by the monthly GPCP-merged analyses so that the adjusted pentad analyses match the monthly GPCP in magnitude while the high-frequency components in the pentad CMAP are retained. The adjusted analyses, called the GPCP-merged analyses of pentad precipitation, are compared to several gauge-based datasets. The results show that the pentad GPCP analyses reproduced spatial distribution patterns of total precipitation and temporal variations of submonthly scales with relatively high quality especially over land. Simple applications of the 23-yr dataset demonstrate that it is useful in monitoring and diagnosing intraseasonal variability. The Pentad GPCP has been accepted by the GPCP as one of its official products and is being updated on a quasi-real-time basis.

[1] Although the advanced microwave sounding unit (AMSU) on board the NOAA 15 and NOAA 16 satellites is primarily designed for profiling atmospheric temperature and moisture, the products associated with clouds and precipitation are also derived using its window channel measurements with a quality similar to those derived from microwave imagers such as the Special Sensor Microwave Imager. However, the AMSU asymmetry in radiance along the scan was found to be obvious at its window channels and could severely degrade the quality of cloud and precipitation products if not properly corrected. Thus a postlaunch calibration scheme is developed for these channels, and the causes of the asymmetry are analyzed from the AMSU instrument model. A preliminary study shows that the asymmetry may be caused by either the AMSU polarization misalignment or the antenna pointing angle error. A generic radiative transfer model is developed for a single-layered cloud using a two-stream approximation and can be utilized for the retrievals of cloud liquid water (L) and total precipitable water (V), cloud ice water path (IWP), and particle effective diameter (D e ). At the AMSU lower frequencies the scattering from cloud liquid is neglected, and therefore the retrieval of L and V is linearly derived using 23.8 and 31.4 GHz. However, for ice clouds the radiative transfer model is simplified by neglecting the thermal emission, and therefore the retrieval of IWP and D e is analytically derived using the AMSU millimeter wavelength channels at 89 and 150 GHz. These cloud algorithms are tested for the AMSU on board the NOAA 15 and NOAA 16 satellites, and the results are rather promising. It is also found that the AMSUderived cloud ice water path is highly correlated with the surface rain rates and is now directly used to monitor surface precipitation throughout the world.

Abstract. Global monthly rainfall estimates have been produced from over 8 years of measurements from the Defense Meteorological Satellite Program series of special sensor microwave/imagers (SSM/Is) and are analyzed to depict seasonal, annual, and interannual variability. This SSM/I product is one of the components of the blended Global Precipitation Climatology Project rainfall climatology. The primary algorithm used is an 85 GHz scattering-based algorithm over land, and a combined 85 GHz scattering and 19/37 GHz emission over ocean, both of which have been calibrated with ground-based radar data. Errors associated with the SSM/I derived monthly rainfall are characterized through comparisons with various gauge-based, climatological, and other satellite-derived rainfall estimates. During the period of June 1990 to December 1991 the 85 GHz channels aboard the SSM/I failed, so no monthly rainfall estimates are available. An alternative algorithm, using a newly developed 37 GHz scattering approach over land, and emission only over ocean, was developed to obtain a continuous record of rainfall estimates for the entire SSM/I time series. Although the 37 GHz scattering algorithm is sensitive to rain rates in excess of 8 mm/h, the correlation between the 37 and 85 GHz monthly estimates over land can be as high as 0.9 (but varies regionally) when comparing both approaches during a period of useable 85 GHz measurements. The error in the monthly rainfall using this algorithm is typically larger in comparison with measurements from rain gauges. Over ocean the emission only algorithm produces a lesser amount of rain than the scatteringbased algorithm, most likely attributed to the lack of a proper beam-filling correction. During the period of January 1992 to the present there were two SSM/I satellites in full operation, with sampling times of approximately 0600/1800 and 1000/2200 LT. Comparisons between the single and dual satellites are made and are compared with gauge data sets. In general, it is found that the dual-satellite estimates reduce the RMS errors, although the improvements are both regionally and seasonally dependent.