Heading Toward Launch with the Integrated Multi-Satellite Retrievals for GPM (IMERG)

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

An Updated TRMM Composite Climatology of Tropical Rainfall and Its Validation

An updated 15-yr Tropical Rainfall Measuring Mission (TRMM) composite climatology (TCC) is presented and evaluated. This climatology is based on a combination of individual rainfall estimates made with data from the primaryTRMMinstruments: theTRMM Microwave Imager (TMI) and the precipitation radar (PR). This combination climatology of passive microwave retrievals, radar-based retrievals, and an algorithm using both instruments simultaneously provides a consensus TRMM-based estimate of mean precipitation. The dispersion of the three estimates, as indicated by the standard deviation sigma among the estimates, is presented as a measure of confidence in the final estimate and as an estimate of the uncertainty thereof. The procedures utilized by the compositing technique, including adjustments and quality-control measures, are described. The results give a mean value of the TCC of 4.3mm day(exp -1) for the deep tropical ocean beltbetween 10 deg N and 10 deg S, with lower values outside that band. In general, the TCC values confirm ocean estimates from the Global Precipitation Climatology Project (GPCP) analysis, which is based on passive microwave results adjusted for sampling by infrared-based estimates. The pattern of uncertainty estimates shown by sigma is seen to be useful to indicate variations in confidence. Examples include differences between the eastern and western portions of the Pacific Ocean and high values in coastal and mountainous areas. Comparison of the TCC values (and the input products) to gauge analyses over land indicates the value of the radar-based estimates (small biases) and the limitations of the passive microwave algorithm (relatively large biases). Comparison with surface gauge information from western Pacific Ocean atolls shows a negative bias (~16%) for all the TRMM products, although the representativeness of the atoll gauges of open-ocean rainfall is still in question

Quasi-Global Precipitation as Depicted in the GPCPV2.2 and TMPA V7

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

Comparison of GPCP Monthly and Daily Precipitation Estimates with High-Latitude Gauge Observations

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

Improving the Global Precipitation Record: GPCP Version 2.1

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 TRMM Multi-Satellite Precipitation Analysis (TMPA)

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

Connecting Satellite-Based Precipitation Estimates to Users

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 TRMM Multi-satellite Precipitation Analysis (TMPA): Quasi-Global Precipitation Estimates at Fine Scales

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

Enhancements to IMERG in Version 06

No abstract availabl