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The latest releases of Global Precipitation Measurement (GPM) mission products cap five years of vigorous development cycle since the launch of the GPM Core Observatory, and these now provide datasets that are relatively homogeneous across the joint Tropical Rainfall Measuring Mission (TRMM) and GPM eras. Version 06 of the U.S. GPM team's Integrated Multi-satellitE Retrievals for GPM (IMERG) merged precipitation product enforces a consistent intercalibration for all precipitation products computed from individual satellites with the TRMM and GPM Core Observatory sensors as the TRMM- and GPM era calibrators, respectively, and incorporates monthly surface gauge data. The basic IMERG algorithm now features precipitation motion vectors (used to drive the Lagrangian interpolation, or "morphing") that arecomputed by tracking vertically integrated vapor fields analyzed in MERRA2 and GEOS5. This innovation provides globally complete coverage, expanding IMERG's coverage beyond the 60°N-S latitude band provided by IR-based vectors, although we continue to mask out precipitation over snowy/icy surfaces as unreliable. A second innovation is the Quality Index (QI) data field. The half-hourly QI is taken as the approximate Kalman Filter correlation computed in the morphing calculation.

The new V06 IMERG is briefly reviewed, then key data visualizations are shown, including the zonal profile of calibration by GPCP, the long-term time series of ocean estimates for the Final Run (with comparison to TMPA and GPCP SG), the time series of tropical ocean rainrate histogram, diurnal cycle, and atoll validation at the monthly time scale.

The U.S. Global Precipitation Measurement mission (GPM) science team is developing a long-term dataset based on intercalibrated estimates from the international constellation of precipitation-relevant satellites and other data. The Integrated Multi-satellitE Retrievals for GPM (IMERG) merged precipitation product (IMERG) is computed at the half hour, 0.1° x 0.1° resolution globally in three "Runs" Early, Late, and Final (4 hours, 14 hours, and 3.5 months after observation time, respectively). The longer latencies increase the available input data for the resulting estimates, most notably the use of monthly precipitation gauge analyses in the Final run. The Early and Late runs use a climatological gauge adjustment as a proxy for the monthly gauge analyses. At meeting time GPM should be well into computing the new Version 06, which will be the first time IMERG covers the last two decades and routinely provides morphed estimates in polar regions where the surface is snow- and ice-free. In this talk a few salient features of the IMERG algorithm will be summarized, then representative examples of IMERG products will be shown. This starts with basic results, such as animations of near-real-time maps, then extends to preliminary analyses of dataset characteristics. For example, the accumulations during Hurricane Harvey around Houston, Texas, USA, tended to be low, while accumulations along the Texas/Louisiana border to the northeast tended to be high. Furthermore, these opposite-sign differences occurred more or less simultaneously over much of the accumulation period. The working hypothesis is that there were systematic differences in the convective "regime" in the two places. The talk will end with a quick summary of the processing status and the future course of IMERG development.

After five years of development following the launch of the Global Precipitation Measurement (GPM) missionCore Observatory, the GPM data products are now being extended across the joint Tropical Rainfall MeasuringMission (TRMM) and GPM eras. Version 06 of the U.S. GPM team's Integrated Multi-satellitE Retrievals forGPM (IMERG) merged precipitation product provides a consistent intercalibration for all precipitation productscomputed from individual satellites with the TRMM and GPM Core Observatory sensors as the TRMM- andGPM-era calibrators, respectively, and incorporates monthly surface gauge data. One major change in the basicIMERG algorithm for V06 is that precipitation motion vectors (used to drive the quasi-Lagrangian interpolation,or "morphing") are computed by tracking vertically integrated vapor (TQV) fields analyzed in MERRA2 andGEOS5. This innovation provides globally complete coverage, expanding IMERG's coverage beyond the 60N-Slatitude band previously provided by IR-based vectors, although precipitation over snowy/icy surfaces is stillmasked out as unreliable. A second innovation is that the Quality Index (QI) data field computed for the half-hourlydatasets has been refined to include estimates of correlation at microwave overpass times.We will summarize the processing status for V06 IMERG, for which the retrospective processing shouldbe actively advancing at meeting time. We will show early examples of performance. For example, the TQVmotion vectors are typically slightly better than the IR-based vectors at all latitudes. The transition across theTRMM/GPM data boundary will be discussed, including the necessity of filling in the TRMM-based calibrationsover the latitude band 35-65 in each hemisphere. The notional schedule for the eventual retirement of thepredecessor TRMM Multi-satellite Precipitation Analysis (TMPA) multi-satellite dataset will be updated as well.

The U.S. Global Precipitation Measurement mission (GPM) science team is developing a long-term dataset based on intercalibrated estimates from the international constellation of precipitation-relevant satellites and other data. The Integrated Multi-satellitE Retrievals for GPM (IMERG) merged precipitation product (IMERG) is computed at the half hour, 0.1° x 0.1° resolution globally in three "Runs"—Early, Late, and Final (4 hours, 14 hours, and 3.5 months after observation time, respectively). GPM is well into computing the new Version 06, which will be the first time IMERG covers the last two decades and routinely provides morphed estimates in polar regions where the surface is snow- and ice-free.A few salient features of the IMERG algorithm will be summarized, then representative examples of IMERG products will be shown. This starts with basic results, such as animations of maps, then extends to preliminary analyses of dataset characteristics. For example, the diurnal cycle demonstrates improvements over V05.

Efforts to construct long-term global precipitation data sets from the decades-long record of satellite (and other) data historically have fallen into one of two categories. Climate Data Records (CDR), such as the Global Precipitation Climatology Project (GPCP), prioritize homogeneity over fine-scale accuracy. On the other hand, High Resolution Precipitation Products (HRPP), such as the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA), emphasize the use of data from all available satellites. For both types of products, experience has shown that it is critical to choose the appropriate reference standard to intercalibrate an ever-evolving constellation of satellite sensors. Towards this end, we have used passive microwave (PMW) in developing GPCP, and combined PMW-radar in TMPA.The launch of the TRMM follow-on Global Precipitation Measurement (GPM) mission in 2014 gave birth to several nearly-global HRPP's, including the Integrated Multi-satellitE Retrievals for GPM (IMERG). Our strategy in refining IMERG has been to first focus on the tropics and mid-latitudes, where we have relatively high confidence, and only more recently begin to expand into the lower-confidence high latitudes. Similarly, although GPCP has provided nearly-global estimates since its inception in the early 1990's, the advent of sensors such as CloudSat has facilitated new efforts to modernize its estimates at high latitudes.For each of these data sets, it is expected that the final merged estimate should tend to track along with its respective calibration standard. Time series depicting GPCP, TMPA, and IMERG will be presented, in order to demonstrate the extent to which this is so. Additionally, comparisons amongst the products will show areas of agreement and highlight areas where further improvements are needed.

This talk will summarize the shifts in IMERG (Integrated Multi-satellitE Retrievals for GPM (Global Precipitation Measurement)) from Version 03 to 04 in early Spring 2016, and to Version 05 in late Summer 2017. For example, Version 04 replaced approximate pre-launch calibrations with GPM Core Observatory-based calibrations, while Version 05 introduced improved estimates for the primary GPM instrument products (DPR, GMI, and Combined Instrument). In Version 04 the IR estimates were routinely calibrated to the passive microwave estimates. As analysis showed that the Combined Instrument estimates (the IMERG calibration standard) tend to be biased high over land and low over ocean at higher latitudes, in Version 04 we climatologically calibrated IMERG to the Global Precipitation Climatology Project (GPCP) monthly Satellite-Gauge product, except in low- and mid-latitude ocean regions. This calibration leaves the relative time series intact, and only adjusts the mean of the entire series. In Version 05 the primary GPM instrument products have reduced biases, but calibration to GPCP continues to be necessary to achieve the most realistic estimates. Finally, retrospective processing back into the TRMM (Tropical Rainfall Measuring Mission) era is expected in early 2018, after which the legacy TMPA (TRMM Multi-satellite Precipitation Analysis) dataset will be retired.

Beginning in 1997, the Merged Precipitation Group at NASA Goddard has distributed gridded global precipitation products built by combining satellite and surface gauge data. This started with the Global Precipitation Climatology Project (GPCP), then the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA), and recently the Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (GPM) mission (IMERG). This 20+-year (and on-going) activity has yielded an important set of insights and lessons learned for making state-of-the-art precipitation data accessible to the diverse communities of users. Merged-data products critically depend on the input sensors and the retrieval algorithms providing accurate, reliable estimates, but it is also important to provide ancillary information that helps users determine suitability for their application. We typically provide fields of estimated random error, and recently reintroduced the quality index concept at user request. Also at user request we have added a (diagnostic) field of estimated precipitation phase. Over time, increasingly more ancillary fields have been introduced for intermediate products that give expert users insight into the detailed performance of the combination algorithm, such as individual merged microwave and microwave-calibrated infrared estimates, the contributing microwave sensor types, and the relative influence of the infrared estimate.

The Day-l algorithm for computing combined precipitation estimates in GPM is the Integrated Multi-satellitE Retrievals for GPM (IMERG). We plan for the period of record to encompass both the TRMM and GPM eras, and the coverage to extend to fully global as experience is gained in the difficult high-latitude environment. IMERG is being developed as a unified U.S. algorithm that takes advantage of strengths in the three groups that are contributing expertise: 1) the TRMM Multi-satellite Precipitation Analysis (TMPA), which addresses inter-satellite calibration of precipitation estimates and monthly scale combination of satellite and gauge analyses; 2) the CPC Morphing algorithm with Kalman Filtering (KF-CMORPH), which provides quality-weighted time interpolation of precipitation patterns following cloud motion; and 3) the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks using a Cloud Classification System (PERSIANN-CCS), which provides a neural-network-based scheme for generating microwave-calibrated precipitation estimates from geosynchronous infrared brightness temperatures. In this talk we summarize the major building blocks and important design issues driven by user needs and practical data issues. One concept being pioneered by the IMERG team is that the code system should produce estimates for the same time period but at different latencies to support the requirements of different groups of users. Another user requirement is that all these runs must be reprocessed as new IMERG versions are introduced. IMERG's status at meeting time will be summarized, and the processing scenario in the transition from TRMM to GPM will be laid out. Initially, IMERG will be run with TRMM-based calibration, and then a conversion to a GPM-based calibration will be employed after the GPM sensor products are validated. A complete reprocessing will be computed, which will complete the transition from TMPA.

After a lengthy incubation period, the year 2012 saw the release of the Global Precipitation Climatology Project (GPCP) Version 2.2 monthly dataset and the TRMM Multi-satellite Precipitation Analysis (TMPA) Version 7. One primary feature of the new data sets is that DMSP SSMIS data are now used, which entailed a great deal of development work to overcome calibration issues. In addition, the GPCP V2.2 included a slight upgrade to the gauge analysis input datasets, particularly over China, while the TMPA V7 saw more-substantial upgrades: 1) The gauge analysis record in Version 6 used the (older) GPCP monitoring product through April 2005 and the CAMS analysis thereafter, which introduced an inhomogeneity. Version 7 uses the Version 6 GPCC Full analysis, switching to the Version 4 Monitoring analysis thereafter. 2) The inhomogeneously processed AMSU record in Version 6 is uniformly processed in Version 7. 3) The TMI and SSMI input data have been upgraded to the GPROF2010 algorithm. The global-change, water cycle, and other user communities are acutely interested in how these data sets compare, as consistency between differently processed, long-term, quasi-global data sets provides some assurance that the statistics computed from them provide a good representation of the atmosphere's behavior. Within resolution differences, the two data sets agree well over land as the gauge data (which tend to dominate the land results) are the same in both. Over ocean the results differ more because the satellite products used for calibration are based on very different algorithms and the dominant input data sets are different. The time series of tropical (30 N-S) ocean average precipitation shows that the TMPA V7 follows the TMI-PR Combined Product calibrator, although running approximately 5% higher on average. The GPCP and TMPA time series are fairly consistent, although the GPCP runs approximately 10% lower than the TMPA, and has a somewhat larger interannual variation. As well, the GPCP and TMPA interannual variations have an apparent phase shift, with GPCP running a few months later. Additional diagnostics will include mean maps and selected scatter plots.

The Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) is intended to provide a "best" estimate of quasi-global precipitation from the wide variety of modern satellite-borne precipitation-related sensors. Estimates are provided at relatively fine scales (0.25 deg x 0.25 deg. 3-h) in both real and post-real time to accommodate a wide range of researchers. However, the errors inherent in the finest scale estimates are large. The most successful use of the TMPA data is when the analysis takes advantage of the fine-scale data to create time/space averages appropriate to the user fs application. We review the conceptual basis for the TMPA, summarize the processing sequence, and focus on two new activities. First, a recent upgrade for the real-time version incorporates several additional satellite data sources and employs monthly climatological adjustments to approximate the bias characteristics of the research quality post-real-time product. Second, an upgrade for the research quality post-real-time TMPA from Versions 6 to 7 (in beta test at press time) is designed to provide a variety of improvements that increase the list of input data sets and correct several issues. Future enhancements for the TMPA will include improved error estimation, extension to higher latitudes, and a shift to a Lagrangian time interpolation scheme.

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 enforce consistency of the submonthly estimates 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.

The Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) is intended to provide a "best" estimate of quasi-global precipitation from the wide variety of modern satellite-borne precipitation-related sensors. Estimates are provided at relatively fine scales (0.25degx0.25deg, 3-hourly) in both real and post-real time to accommodate a wide range of researchers. However, the errors inherent in the finest scale estimates are large. The most successful use of the TMPA data is when the analysis takes advantage of the fine-scale data to create time/space averages appropriate to the user s application. We review the conceptual basis for the TMPA, summarize the processing sequence, and focus on two new activities. First, a recent upgrade to the real-time version incorporates several additional satellite data sources and employs monthly climatological adjustments to approximate the bias characteristics of the research quality post-real-time product. Second, an upgrade of the research quality post-real-time TMPA from Version 6 to Version 7 (in beta test at press time) is designed to provide a variety of improvements that increase the list of input data sets and correct several issues. Future enhancements for the TMPA will include improved error estimation, extension to higher latitudes, and a shift to a Lagrangian time interpolation scheme.

It is very important to know how much rain and snow falls around the world for uses that range from crop forecasting to disaster response, drought monitoring to flood forecasting, and weather analysis to climate research. Precipitation is usually measured with rain gauges, but rain gauges don t exist in areas that are sparsely populated, which tends to be a good portion of the globe. To overcome this, meteorologists use satellite data to estimate global precipitation. However, it is difficult to estimate rain and especially snow in cold climates using most current satellites. The satellite sensors are often "confused" by a snowy or frozen surface and therefore cannot distinguish precipitation. One commonly used satellite-based precipitation data set, the Global Precipitation Climatology Project (GPCP) data, overcomes this frozen-surface problem through the innovative use of two sources of satellite data, the Television Infrared Observation Satellite Operational Vertical Sounder (TOVS) and the Atmospheric Infrared Sounder (AIRS). Though the GPCP estimates are generally considered a very reliable source of precipitation, it has been difficult to assess the quality of these estimates in cold climates due to the lack of gauges. Recently, the Finnish Meteorological Institute (FMI) has provided a 12-year span of high-quality daily rain gauge observations, covering all of Finland, that can be used to compare with the GPCP data to determine how well the satellites estimate cold-climate precipitation. Comparison of the monthly GPCP satellite-based estimates and the FMI gauge observations shows remarkably good agreement, with the GPCP estimates being 6% lower in the amount of precipitation than the FMI observations. Furthermore, the month-to-month correlation between the GPCP and FMI is very high at 0.95 (1.0 is perfect). The daily GPCP estimates replicate the FMI daily occurrences of precipitation with a correlation of 0.55 in the summer and 0.45 in the winter. The winter result indicates the GPCP estimates have skill in "seeing" snowfall, which is the most challenging situation. Thus, the GPCP data set successfully overcomes a current limitation in satellite meteorology, namely the estimation of cold-climate precipitation. The success of the GPCP data set bodes well for future missions, whose instrumentation is specifically designed to give even more information for addressing cold-climate precipitation.

The TRMM Multi-satellite Precipitation Analysis (TMPA) provides a calibration-based sequential scheme for combining multiple precipitation estimates from satellites, as well as gauge analyses where feasible, at fine scales (0.25 degrees x 0.25 degrees and 3-hourly). It 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 data set covers the latitude band 50 degrees N-S for the period 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 in one of the input products (based on 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 fine-scale estimators. Examples are provided of a flood event and diurnal cycle determination.

Satellite data form the core of the information available for estimating precipitation on a global basis. While it is possible to create such estimates solely from one sensor, researchers have increasingly moved to using combinations of sensors in an attempt to improve accuracy, coverage, and resolution. This poster updates a long-term project in which the authors are working to provide routine combined-sensor estimates of precipitation over the entire globe at relatively fine time and space intervals. The goal is to produce these globally complete precipitation estimates on a 25-km grid every 3 hours. Since late January 2002 we have been estimating precipitation for the latitude band 50 degrees N-S within about 6 hours of observation time. This work is 1 of only 2 or 3 such efforts in the world. Now we are preparing to provide similar estimates for the last 5 years. All of this work is being carried out as part of the Tropical Rainfall Measuring Mission (TRMM). Initially, TRMM was focused on providing excellent long-term averages of precipitation in tropical regions, but since its launch in November 1997 continued research has allowed the same satellite and data system to be used for addressing weather-scale problems as well.

One of the difficulties in studying landfalling extratropical cyclones along the Pacific Coast is the lack of antecedent data over the ocean, including precipitation. Recent research on combining various satellite-based precipitation estimates opens the possibility of realistic precipitation estimates on a global 1 deg. x 1 deg. latitude-longitude grid at the daily or even 3-hourly interval. The goal in this work is to provide quantitative precipitation estimates that correctly represent the precipitation- related variables in the hydrological cycle: surface accumulations (fresh-water flux into oceans), frequency and duration statistics, net latent heating, etc.

Multi-purpose remote-sensing products from various satellites have proved crucial in developing global estimates of precipitation. Examples of these products include low-earth-orbit and geosynchronous-orbit infrared (leo- and geo-IR), Outgoing Longwave Radiation (OLR), Television Infrared Operational Satellite (TIROS) Operational Vertical Sounder (TOVS) data, and passive microwave data such as that from the Special Sensor Microwave/ Imager (SSM/I). Each of these datasets has served as the basis for at least one useful quasi-global precipitation estimation algorithm; however, the quality of estimates varies tremendously among the algorithms for the different climatic regions around the globe.

The One-Degree Daily (1DD) precipitation dataset has been developed for the Global Precipitation Climatology Project (GPCP) and is currently in beta test preparatory to release as an official GPCP product. The 1DD provides a globally-complete, observation-only estimate of precipitation on a daily 1 deg. x 1 deg. grid for the period 1997 through early 2000 (by the time of the conference). In the latitude band 40N-40S the 1DD uses the Threshold-Matched Precipitation Index (TMPI), a GPI-like IR product with the pixel-level T(sub b) threshold and (single) conditional rain rate determined locally for each month by the frequency of precipitation in the GPROF SSM/I product and by, the precipitation amount in the GPCP monthly satellite-gauge (SG) combination. Outside 40N-40S the 1DD uses a scaled TOVS precipitation estimate that has month-by-month adjustments based on the TMPI and the SG. Early validation results are encouraging. The 1DD shows relatively large scatter about the daily validation values in individual grid boxes, as expected for a technique that depends on cloud-sensing schemes such as the TMPI and TOVS. On the other hand, the time series of 1DD shows good correlation with validation in individual boxes. For example, the 1997-1998 time series of 1DD and Oklahoma Mesonet values in a grid box in northeastern Oklahoma have the correlation coefficient = 0.73. Looking more carefully at these two time series, the number of raining days for the 1DD is within 7% of the Mesonet value, while the distribution of daily rain values is very similar. Other tests indicate that area- or time-averaging improve the error characteristics, making the data set highly attractive to users interested in stream flow, short-term regional climatology, and model comparisons. The second generation of the 1DD product is currently under development; it is designed to directly incorporate TRMM and other high-quality precipitation estimates. These data are generally sparse because they are observed by low-orbit satellites, so a fair amount of work must be devoted to analyzing the effect of data boundaries. This work is laying, the groundwork for effective use of the NASA Global Precipitation Mission, which will have full Global coverage by low-orbit passive microwave satellites every three hours.

Two globally-complete, observation-only precipitation datasets have recently been developed for the Global Precipitation Climatology Project (GPCP). Both depend heavily on a variety of satellite input, as well as gauge data over land. The first, Version 2x79, provides monthly estimates on a 2.5 deg. x 2.5 deg. lat/long grid for the period 1979 through late 1999 (by the time of the conference). The second, the One-Degree Daily (1DD), provides daily estimates on a 1 deg. x l deg. grid for the period 1997 through late 1999 (by the time of the conference). Both are in beta test preparatory to release as official GPCP products. These datasets provide a unique perspective on the hydrological effects of the various atmospheric flow anomalies that have been identified by meteorologists. In this paper we discuss the regional precipitation effects that result from persistent extratropical flow anomalies. We will focus on the Pacific-North America (PNA) and North Atlantic Oscillation (NAO) patterns. Each characteristically becomes established on synoptic time scales, but then persists for periods that can exceed a month. The onset phase of each appears to have systematic mobile features, while the mature phase tend to be more stationary. Accordingly, composites of monthly data for outstanding positive and negative events (separately) contained in the 20-year record reveal the climatological structure of the precipitation during the mature phase. The climatological anomalies of the positive, negative, and (positive-negative) composites show the expected storm-track-related shifts in precipitation, and provide the advantage of putting the known precipitation effects over land in the context of the total pattern over land and ocean. As well, this global perspective points out some unexpected areas of correlation. Day-by-day composites of daily data anchored to the onset date demonstrate the systematic features during the onset. Although the 1DD has a fairly short record, some preliminary results are shown and compared to previous work with numerical weather prediction models.

The One-Degree Daily (1DD) precipitation dataset was recently developed for the Global Precipitation Climatology Project (GPCP). The IDD provides a globally-complete, observation-only estimate of precipitation on a daily 1 deg x 1 deg grid for the period 1997 through late 1999 (by the time of the conference). In the latitude band 40 N - 40 S the IDD uses the Threshold-Matched Precipitation Index (TMPI), a GPI-like IR product with the T(sub b) threshold and (single) conditional rain rate determined locally for each month by the frequency of precipitation in the GPROF SSNU product and by the precipitation amount in the GPCP satellite-gauge (SG) combination. Outside 40 N - 40 S the 1DD uses a scaled TOVS precipitation estimate that has adjustments based on the TMPI and the SG. This first-generation 1DD has been in beta test preparatory to release as an official GPCP product. In this paper we discuss further development of the 1DD framework to allow the direct incorporation of TRMM and other high-quality precipitation estimates. First, these data are generally sparse (typically from low-orbit satellites), so a fair amount of work was devoted to data boundaries. Second, these data are not the same as the original 1DD estimates, so we had to give careful consideration to the best scheme for forcing the 1DD to sum to the SG for the month. Finally, the non-sun-synchronous, low-inclination orbit occupied by TRMM creates interesting variations against the sun-synchronous, high-inclination orbits occupied by the Defense Meteorological Satellite Program satellites that carry the SSM/I. Examples will be given of each of the development issues, then comparisons will be made to daily raingauge analyses.

Abstract A technique is described to use Tropical Rain Measuring Mission (TRMM) combined radar/radiometer information to adjust geosynchronous infrared satellite data (the TRMM Adjusted GOES Precipitation Index, or TRMM AGPI). The AGPI is then merged with rain gauge information (mostly over land; the TRMM merged product) to provide fine- scale (1 deg latitude/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 deg N-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 over ocean and 3.1 mm/ day 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, 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 in to 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 data sets shows varied results; the bias is near zero when compared to western Pacific Ocean atoll raingauge data, but significantly positive compared to 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 ENSO tropical precipitation pattern from early 1998 (El Nino) through early 1999 (La Nina) and beyond. The El Nino minus La Nina difference map shows the eastern Pacific maximum, the maritime continent minima and other tropical and mid-latitude features. The differences in the Pacific are 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 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.

Two globally-complete, observation-only precipitation datasets have recently been developed for the Global Precipitation Climatology Project (GPCP). Both depend heavily on a variety of satellite input, as well as gauge data over land. The first, Version 2 x 79, provides monthly estimates on a 2.5 deg x 2.5 deg lat/long grid for the period 1979 through late 1999 (by the time of the conference). The second, the One-Degree Daily (1DD), provides daily estimates on a 1 deg x 1 deg grid for the period 1997 through late 1999 (by the time of the conference). Both are in beta test preparatory to release as official GPCP products. These datasets provide a unique perspective on the hydrological effects of the various atmospheric flow anomalies that have been identified by meteorologists. In this paper we discuss the regional precipitation effects that result from persistent extratropical flow anomalies. We will focus on the Pacific-North America (PNA) and North Atlantic Oscillation (NAO) patterns. Each characteristically becomes established on synoptic time scales, but then persists for periods that can exceed a month. The onset phase of each appears to have systematic mobile features, while the mature phase tend to be more stationary. Accordingly, composites of monthly data for outstanding positive and negative events (separately) contained in the 20-year record reveal the climatological structure of the precipitation during the mature phase. The climatological anomalies of the positive, negative, and (positive-negative) composites show the expected storm-track-related shifts in precipitation, and provide the advantage of putting the known precipitation effects over land in the context of the total pattern over land and ocean. As well, this global perspective points out some unexpected areas of correlation. Day-by-day composites of daily data anchored to the onset date demonstrate the systematic features during the onset. Although the 1DD has a fairly short record, some preliminary results are shown and compared to previous work with numerical weather prediction models.