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NASA NTRS2007

Status and Plans for the WCRP/GEWEX Global Precipitation Climatology Project (GPCP)

The Global Precipitation Climatology Project (GPCP) is an international project under the auspices of the World Climate Research Program (WCRP) and GEWEX (Global Water and Energy Experiment). The GPCP group consists of scientists from agencies and universities in various countries that work together to produce a set of global precipitation analyses at time scales of monthly, pentad, and daily. The status of the current products will be briefly summarized, focusing on the monthly analysis. Global and large regional rainfall variations and possible long-term changes are examined using the 27-year (1 979-2005) monthly dataset. In addition to global patterns associated with phenomena such as ENSO, the data set is explored for evidence of long-term change. Although the global change of precipitation in the data set is near zero, the data set does indicate a small upward change in the Tropics (25s-25N) during the period,. especially over ocean. Techniques are derived to isolate and eliminate variations due to ENS0 and major volcanic eruptions and the significance of the linear change is examined. Plans for a GPCP reprocessing for a Version 3 of products, potentially including a fine-time resolution product will be discussed. Current and future links to IPWG will also be addressed.

Adler, Robert F.↗
NASA NTRS2007

The Global Precipitation Climatology Project (GPCP): Results, Status and Future

The Global Precipitation Climatology Project (GPCP) is one of a number of long-term, satellite-based, global analyses routinely produced under the auspices of the World Climate Research Program (WCRP) and its Global Energy and Watercycle EXperiment (GEWEX) program. The research quality analyses are produced a few months after real-time through the efforts of scientists at various national agencies and universities in the U.S., Europe and Japan. The primary product is a monthly analysis of surface precipitation that is globally complete and spans the period 1979-present. There are also pentad analyses for the same period and a daily analysis for the 1997-present period. Although generated with somewhat different data sets and analysis schemes, the pentad and daily data sets are forced to agree with the primary monthly analysis on a grid box by grid box basis. The primary input data sets are from low-orbit passive microwave observations, geostationary infrared observations and surface raingauge information. Examples of research with the data sets are discussed, focusing on tropical (25N-25s) rainfall variations and possible long-term changes in the 28-year (1979-2006) monthly dataset. Techniques are used to discriminate among the variations due to ENSO, volcanic events and possible long-term changes for rainfall over both land and ocean. The impact of the two major volcanic eruptions over the past 25 years is estimated to be about a 5% maximum reduction in tropical rainfall during each event. Although the global change of precipitation in the data set is near zero, a small upward linear change over tropical ocean (0.06 mm/day/l0yr) and a slight downward linear change over tropical land (-0.03 mm/day/l0yr) are examined to understand the impact of the inhomogeneity in the data record and the length of the data set. These positive changes correspond to about a 5% increase (ocean) and 3% increase (ocean plus land) during this time period. Relations between variations in surface temperature and precipitation are analyzed on seasonal to inter-decadal time scales. A new, version 3 of GPCP is being planned to incorporate new satellite information (e.g., TRMM) and provide higher spatial and temporal resolution for at least part of the data record. The goals and plans for that GPCP re-processing will be outlined.

Adler, Robert F.↗
NASA NTRSDate not supplied

IMERG and GPCP Seasonality and Response to Climate and Weather Variability

The Integrated Multi-satellitE Retrievals for GPM (IMERG) and the The Global Precipitation Climatology Project (GPCP) are two of the most popular precipitation products. IMERG is a relatively new dataset that targets the needs primarily of the hydrological community by resolving the hydrological cycle of precipitation at fine temporal (30-minutes) and spatial (10-km) scales. IMERG only recently exceeded the user base of the highly successful, but discontinued in 2019, TRMM Multi-satellite Precipitation Analysis (TMPA). GPCP, on the other hand, has traditionally been strong in the climate research community, and recently has been revised under the framework of NASA's Making Earth System Data records for Use in Research Environments (MEaSUREs) program. Both IMERG and GPCP are similar in the underlying approaches to achieve global coverage, in particular using satellite microwave and infrared observations, and adjusting the precipitation retrieval with rain gauge information. While there is a tendency to use both datasets interchangeably, differences remain and some of them limit the usage of IMERG as a climate data record at this point. By applying Principal Component Analysis, we identify the differences and similarities mode-by-mode, and further the guidance on suggested usage of IMERG as a climate data record. As an example in the attached figure, IMERG vs GPSP differences in the explained variance by the two leading seasonal modes can be identified, mainly in the extreme southern latitudes, and over western boundary currents (Gulfstream and Kuroshio). At the preparation time for this presentation, the new version "07"of IMERG was in the works that may resolve the issues presented here. Nevertheless, our analysis can help to gauge the uncertainties of the studies already done using the currently existing IMERG version "06", and evaluate the improvements in the upcoming version "07".

Andrey Savtchenko↗
NASA NTRS2012

Estimating Climatological Bias Errors for the Global Precipitation Climatology Project (GPCP)

A procedure is described to estimate bias errors for mean precipitation by using multiple estimates from different algorithms, satellite sources, and merged products. The Global Precipitation Climatology Project (GPCP) monthly product is used as a base precipitation estimate, with other input products included when they are within +/- 50% of the GPCP estimates on a zonal-mean basis (ocean and land separately). The standard deviation s of the included products is then taken to be the estimated systematic, or bias, error. The results allow one to examine monthly climatologies and the annual climatology, producing maps of estimated bias errors, zonal-mean errors, and estimated errors over large areas such as ocean and land for both the tropics and the globe. For ocean areas, where there is the largest question as to absolute magnitude of precipitation, the analysis shows spatial variations in the estimated bias errors, indicating areas where one should have more or less confidence in the mean precipitation estimates. In the tropics, relative bias error estimates (s/m, where m is the mean precipitation) over the eastern Pacific Ocean are as large as 20%, as compared with 10%-15% in the western Pacific part of the ITCZ. An examination of latitudinal differences over ocean clearly shows an increase in estimated bias error at higher latitudes, reaching up to 50%. Over land, the error estimates also locate regions of potential problems in the tropics and larger cold-season errors at high latitudes that are due to snow. An empirical technique to area average the gridded errors (s) is described that allows one to make error estimates for arbitrary areas and for the tropics and the globe (land and ocean separately, and combined). Over the tropics this calculation leads to a relative error estimate for tropical land and ocean combined of 7%, which is considered to be an upper bound because of the lack of sign-of-the-error canceling when integrating over different areas with a different number of input products. For the globe the calculated relative error estimate from this study is about 9%, which is also probably a slight overestimate. These tropical and global estimated bias errors provide one estimate of the current state of knowledge of the planet's mean precipitation.

Global Precipitation Climatology Project (GPCP)↗
NASA NTRS1997

The Global Precipitation Climatology Project (GPCP) Combined Precipitation Dataset

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 deg x 2.5 deg 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.

Huffman, George J.↗
NASA NTRS1999

Tropical Rainfall Analysis Using TRMM in Combination With Other Satellite Gauge Data: Comparison with Global Precipitation Climatology Project (GPCP) Results

This paper describes recent results of using Tropical Rainfall Measuring Mission (TRMM) information as the key calibration tool in a merged analysis on a 1 deg x 1 deg latitude/longitude monthly scale based on multiple satellite sources and raingauge analysis. The procedure used to produce the GPCP data set is a stepwise approach which first combines the satellite low-orbit microwave and geosynchronous IR observations into a "multi-satellite" product and than merges that result with the raingauge analysis. Preliminary results produced with the still-stabilizing TRMM algorithms indicate that TRMM shows tighter spatial gradients in tropical rain maxima with higher peaks in the center of the maxima. The TRMM analyses will be used to evaluate the evolution of the 1998 ENSO variations, again in comparison with the GPCP analyses.

Adler, Robert F.↗
NASA NTRS2000

Applications of the GPCP Satellite-Gauge Merged Precipitation Data Set to the Goals of PACS and GCIP

Recently, under the direction of GEWEX/GCIP there has been an attempt to link North American monsoon rainfall to precipitation in the Great Plains for the purposes of predictability on the seasonal time scale. Rain gauge analyses have been used to show continental-scale relationships. However, the interannual variability of warm season precipitation over the United States is linked to the east Pacific climate system. The Global Precipitation Climatology Project (GPCP) satellite estimates of precipitation over oceans add a vital piece of information towards the goals of PACS and GCIP. This preliminary study will begin by focusing on the East Pacific sector. El-Nino-Southern Ossilation (ENSO) modulates the position and strength of the Inter-Tropical Convergence Zone (ITCZ), which affects the precipitation source for the Mexican monsoon. For example, the five driest summers in coastal Mexico occurred during the five strongest El Ninos (as determined by positive values of Nino 3.4) since 1979. Previous work has investigated lag-lead relationships between the North American monsoon and continental rainfall. GPCP has the potential to extend these relationships to a global scale. Finally data sets with a finer time and space scale, including Tropical Rainfall Measurement Mission (TRMM) products, will be used to examine wave dynamics. Kelvin waves, in particular, may be trigger mechanisms for the onset of the North American monsoon.

Curtis, Scott↗
NASA NTRS2001

Seasonal Variation in Precipitation Patterns to the Global Ocean: An Analysis of the GPCP Version 2 Data Set

An analysis of temporal and spatial variation of oceanic precipitation was conducted on the GPCP version two data set. While the precipitation patterns observed are generally similar to the previous climatologies, new features and greater detail of global precipitation were revealed from out analysis of the GPCP data set. High precipitation waw observed in the inter-tropical convergence zone, the South Pacific convergence zone, and the storm tracks in the North Pacific and Atlantic Oceans. Low precipitation was observe in the Polar regions and in the subtropics of the East Pacific, East Atlantic, and the Southeast and Northwest Indian Ocean. The spatial coverage of these high and low precipitation regions changed thruough the year. A strong seasonal cycle or precipitation was observed for the Northern and the Southern Hemispheres and for each ocean basin. Global precipitation also varied significantly with both latitude and longitude, with a latitudinal maximum at 56 degrees South, 39 degrees South, 4 degrees South, 6 degrees North, 39 degrees North, and 56 degress North, and a longitudinal maxiumum over each ocean. The seasonal varying precipitation patterns are a foundation for evaluating the effect of wet deposition on ocean circulation, flux of chemical species, and its effect on marine ecosystems.

Miller, Richard↗
NASA NTRS2009

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.

Huffman, George J.↗
NASA NTRS1990

A combined microwave/infrared algorithm for estimating rainfall during the GPCP

The paper presents results of a satellite algorithm intercomparison of monthly precipitation, which was organized by the World Climate Research Program's Global Precipitation Climatology Project (GPCP). Special attention is given to the techniques used in the projects and the type of data provided in the study (mainly by Japan's GMS visible and IR sensors and the USA's Special Sensor Microwave/Imager). The results of rainfall estimates obtained by Negri et al. (1994) and Adler and Negri (1988) techniques are compared with estimates made with the threshold technique of Arkin (1979, 1983). Results obtained by various techniques are presented for both the instantaneous estimates and for total rain accumulations over an area including Japan for a 24-hr period on June 22, 1989.

Negri, Andrew J.↗
NASA NTRS2000

The Evolution of Tropical Precipitation Patterns During ENSO Events Using 21+ Years of GPCP Merged Data

The ENSO phenomenon is characterized by fluctuations in the climate system of the tropical Pacific. Quantifying changes in the precipitation component of this system is important in understanding the distribution of heating in the atmosphere which drives the large-scale circulation and affects the weather patterns in the mid-latitudes. Monitoring precipitation anomalies in the Pacific is also an important component for tracking the evolution of ENSO. The most timely and complete observations of the earth come from satellite instruments. In this study, the state of the art satellite-gauge merged monthly precipitation data set from the Global Precipitation Climatology Project (GPCP) is used to depict tropical rainfall patterns during ENSO events over the past two decades and quantify these patterns using indices. This analysis will be complemented by daily precipitation data which can resolve the Madden-Julian Oscillation and westerly wind burst events. The 1997-98 El Nino and 1998-2000 La Nina were the best observed ENSO cycle in the historic record. Prior to the El Nino (in terms of anomalous warming of the east Pacific) dry anomalies over the Maritime Continent were observed in February 1997 as a westerly wind burst advected convection to the east. The largest SST anomalies occurred around November-December 1997, which were followed by the largest precipitation anomalies in the beginning of 1998. The largest precipitation departures from normal were not colocated with the SST anomalies, but were further west, In the spring of 1998 negative precipitation anomalies to the north of the equator intensified, signaling the mature phase of the El Nino. A rapid increase in the precipitation-based La Nina index from December-January 1998 to March-April 1998 signaled the coming La Nina. The 1982-1983 El Nino was comparable in strength (according to several indices) and the precipitation patterns evolved in a similar fashion. For the 1998-2000 La Nina, the coldest anomalies, were confined to the central equatorial Pacific, while the driest anomalies were found in the west Pacific,

Curtis, Scott↗
NASA NTRS2000

Global Patterns of Precipitation Anomalies Related to ENSO as Determined by the 20-Year GPCP Analysis

The new 20-year, monthly, globally complete precipitation analysis of the Global Precipitation Climatology Project (GPCP) is used to analyze ENSO-related precipitation anomalies over the globe. This Version 2 of the community generated data set is global, monthly, at 2.5 deg x 2.5 deg latitude-longitude resolution and utilizes precipitation estimates from low-orbit microwave sensors (SSM/I) and geosynchronous IR sensors and raingauge information over land. In the 1987-present period the low-orbit microwave (SSM/I) estimates are used to adjust or correct the geosynchronous IR estimates, thereby maximizing the utility of the more physically-based microwave estimates and the finer time sampling of the geosynchronous observations. Information from raingauges is blended into the analyses over land. The extension back to 1979 utilizes the OLR Precipitation Index (OPI) for the satellite component. An ENSO Precipitation Index (ESPI) using gradients of precipitation anomalies in the Maritime-Continent/Pacific Ocean region is used to define El Nino/La Nina months during the 20-year record. Mean anomalies for El Nino and La Nina are examined along with variations with respect to season and for individual events. The El Nino and La Nina mean anomalies are near mirror images of each other and when combined produce an ENSO signal with significant spatial continuity over large distances. This El Nino minus La Nina standardized precipitation anomaly map shows the usual positive anomaly over the central and eastern Pacific Ocean with the negative anomaly over the maritime continent along with an additional negative anomaly over Brazil and the Atlantic Ocean extending into Africa and a positive anomaly over the Horn of Africa and the western Indian Ocean. From these features along the Equator narrow positive and negative anomalies extend into middle latitudes in a V-shaped pattern open to the East as described by previous investigators. A number of the features are shown to continue into high latitudes. Positive anomalies extend in the Southern Hemisphere (S.H.) from the Pacific southeastward across Chile and Argentina into the south Atlantic Ocean. In the Northern Hemisphere (N.H.) the counterpart feature extends across the southern U.S. and Atlantic Ocean into Europe. Further to the west a negative anomaly extends southeastward again from the Maritime Continent across the South Pacific and through the Drake Passage. The N.H. counterpart crosses the North Pacific and southern Canada. Other features seemingly extend into Antarctica and into the Arctic Ocean.

Adler, Robert↗
NASA NTRS2003

Estimating Uncertainty in GPCP and TRMM Multi-Satellite Precipitation Estimates

One of the high-priority problems in satellite precipitation estimation is developing algorithms for estimating the errors in precipitation retrievals by individual sensors and subsequent multi-satellite combinations. Classically, we distinguish between "random" and "bias" errors, which do and do not, respectively average to zero over a "big enough" time/space sample. The current operational GPCP and TRMM multi-satellite algorithms are nearly unique in estimating random error for the monthly precipitation estimates from individual sensor systems (including gauge), following Huffman, and then making multi-sensor combinations. No routinely operational global precipitation produces estimates of bias error. Subsequently, a similar scheme has been followed to provide random error estimates for the Multi-satellite Precipitation Analysis (MPA) being computed in real time and after real time for TRMM. The Huffman algorithm for random error is briefly reviewed, including a discussion of the limitations imposed on the algorithm by standard monthly precipitation data sets. Starting from a very simple theoretical treatment of the histogram of precipitation samples in a month, an equation is developed that depends on the estimated average precipitation rate for the month, the number of samples in the month, and two constants. The constants are set separately for each source of precipitation estimate (such as "raingauge") by calibration at selected ground sites. We discuss recent work validating the random error estimates to highlight the successes and limitations of this first-generation approach. We then consider what information is needed from the individual sensor algorithms to facilitate additional accuracy in the estimation of random errors across the time/space span of climate regimes which a global estimation system must handle. In addition, the thorny issue of estimating bias is raised. Finally, the role of error estimates (and the qualitative errors!) in creating combinations of precipitation estimates from different individual sensors is discussed. This issue is particularly important when fine scales in space and time are being considered, say the 0.25 x 0.25-deg 3-hourly estimates in the MPA.

Huffman, G. J.↗
NASA NTRS2006

Tropical Rainfall Variability on Interannual-to-Interdecadal/Longer-Time Scales Derived from the GPCP Monthly Product

Global and large regional rainfall variations and possible long-term changes are examined using the 26-year (1979-2004) GPCP monthly dataset (Adler et al., 2003). Our emphasis is to discriminate among variations due to ENSO, volcanic events, and possible long-term climate changes in the tropics. Although the global linear change of precipitation in the data set is near zero during the time period, an increase in tropical rainfall is noted, with a weaker decrease over northern hemisphere middle latitudes. Focusing on the tropics (25degS-25degN), the data set indicates an upward trend (0.06 mm/day/decade) and a downward trend (-0.02 mm/day/decade) over tropical ocean and land, respectively. This corresponds to an about 4.9% increase (ocean) and 1.6% decrease (land) during the entire 26-year time period. Techniques are applied to isolate and quantify variations due to ENSO and two major volcanic eruptions (El Chichon, March 1982; Pinatubo, June 1991) in order to examine longer time-scale changes. The ENSO events generally do not impact the tropical total rainfall, but, of course, induce significant anomalies with opposite signs over tropical land and ocean. The impact of the two volcanic eruptions is estimated to be about a 5% reduction in tropical rainfall over both land and ocean. A modified data set (with ENSO and volcano effects removed) retains the same approximate linear change slopes, but with reduced variance, thereby increasing the confidence levels associated with the long-term rainfall changes in the tropics 2

Gu, Guojun↗
NASA NTRSDate not supplied

GPCP Version 3.2 Products and Results

The Global Precipitation Climatology Project (GPCP) products address the need for long-term precipitation products that emphasize homogeneity, following Climate Data Record (CDR) principles. The new-generation Version 3.2 provides key improvements over the operational Version 2.3 such as: finer spatial resolution of 0.5°x0.5°; wider geosynchronous infrared estimation (58°N-S) upgraded with the PERSIANN-CDR algorithm; upgraded retrievals from selected passive microwave sensors (GPROF algorithm) that calibrate the IR input; revised intercalibrations of TOVS and AIRS data (used at high latitudes); climatologies based on CloudSat, TRMM, and GPM to provide overall calibration by modern satellite estimates; the latest Global Precipitation Climatology Centre (GPCC) precipitation gauge analyses over land areas; regional modifications to the gauge undercatch correction; and IMERG half-hourly data input to the Daily V3.2 product. We will show sample analyses that demonstrate aspects of the Version 3.2 precipitation record, such as the global climatology, the time series for global land and ocean total precipitation and snowfall, and the time series of tropical land and ocean daily precipitation rate histograms. For selected analyses we will show improvements in both the Monthly and Daily products in Version 3.2 compared to the operational Version 2.3. In particular, the climatological zonal profile of precipitation in the Southern Ocean, extending south of 40°S, improves a suspected artifact in V2.3. Similarly, the Daily histograms over ocean in Version 3.2 lack the jump in the predecessor Version 1.3 Daily over ocean at the start of 2009, although a smaller jump is introduced in June 2014. The presentation will conclude with a prospectus for the future satellites/sensors and community datasets necessary to continue computation of a consistent CDR product on the one hand, while also potentially contributing to improvements in the historical record.

Global Precipitation Measurement↗