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.

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 One-Degree

The GPCP has developed Version 2.1 of its long‐term (1979–present) global Satellite‐Gauge (SG) data sets to take advantage of the improved GPCC gauge analysis, which is one key input. As well, the OPI estimates used in the pre‐SSM/I era have been rescaled to 20 years of the SSM/I‐era SG. The monthly, pentad, and daily GPCP products have been entirely reprocessed, continuing to require that the submonthly estimates sum to the monthly. Version 2.1 is close to Version 2, with the global ocean, land, and total values about 0%, 6%, and 2% higher, respectively. The revised long‐term global precipitation rate is 2.68 mm/d. The corresponding tropical (25°N‐S) increases are 0%, 7%, and 3%. Long‐term linear changes in the data tend to be smaller in Version 2.1, but the statistics are sensitive to the threshold for land/ocean separation and use of the pre‐SSM/I part of the record.

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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.

<p>As part of the “extended operations” past the 3-year prime mission, the Global Precipitation Measurement (GPM) mission continues to develop improved products, currently rolling out the next Version 07 datasets.  This is later than expected, due to unforeseen complications in upgrading algorithms.  Example upgrades include:  Complete data across the shift in scanning strategy by the Dual-frequency Precipitation Radar is now provided.  The Goddard Profiling (GPROF) algorithm is improved in regions where orographic enhancement and suppression take place, or where the surface is snowy/icy.  One key point is ensuring continuity across the boundary between the Tropical Rainfall Measuring Mission (TRMM) and of the GPM Core Observatory for each product.  As well, analyses by users have directly affected algorithm development.  Specifically, user research on precipitation features in the Integrated Multi-satellitE Retrievals for GPM (IMERG) led to findings on how the forward/backward morphing process and Kalman filter (KF) weighting distorts the Probability Density Function (PDF) of regional precipitation rates.  This insight has led to the Scheme for Histogram Adjustment with Ranked Precipitation Estimates in the Neighborhood (SHARPEN), a regional adjustment to the PDF of KF precipitation estimates.  In another initiative, the IMERG team worked with a user to develop the Histogram Anomaly Time Series analysis, providing a simple summary of the time series of anomalies in  the PDF of precipitation over a region, and revealing natural and input-based variations in precipitation. </p><p>We will report the status of GPM Version 07 processing as of the conference time, and provide some examples of the changes in algorithm performance between Versions 06 and 07.</p>