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NASA NTRS2019

A Comparison of the GPROF Algorithm Results in the 2A-CLIM and 2A Data Products

The GPROF algorithm is used to retrieve precipitation values from all the GPM mission partner radiometers (i.e. GMI, MHS, ATMS, SSMIS, AMSR2). GPM produces two precipitation retrieval products (processing at level 2) using the GPROF algorithm. One has the type designation 2A and the other the designation 2A-CLIM. As a result understanding the difference between the two products is important for using and understanding them. Both of these GPROF products use exactly the same algorithm and a-priori databases. The only difference between the two types is the ancillary products used in the initial processing step. In the case of the 2A-CLIM products, ECMWF ERA-I is used as ancillary data. In the case of the 2A products, JMA's GANAL product is used as ancillary data. This presentation will provide a comparison between the two products using zonal mean comparisons. It will provide comparisons separately for land and ocean retrievals. It will compare the GMI differences over the entire current GPM mission life. As the only difference between the two products is the ancillary information used to create them, the comparison provides an elementary sensitivity study of the variables used from the reanalysis products and their impact on the GPROF retrieval. The purpose of the presentation is to provide users with an insight into the two GPROF products and provide them key information useful when using either of the products in scientific studies. Lastly, the presentation also discusses the reasons why only the 2A-CLIM product can be produced for the 17+ years of TRMM era data products.

GPM↗
NASA NTRS2010

Changes in the TRMM Version 7 Space/Time Averaged Level 3 Data Products Based on GPROF TMI Swath-Based Precipitation Retrievals

TRMM has three level 3 (space/time averaged) data products that aggregate level 2 TRMM Microwave Imager (TMI) GPROF precipitation retrievals. These three products are TRMM 3A12, which is a monthly accumulation of 2A12 the GPROF swath retrieval product; TRMM 3B31, which is a monthly accumulation of 2A12 and 2B31 the combined retrieval product that uses both Precipitation Radar (PR) and TMI data; and 3G68 and its variants, which provide hourly retrievals for TMI, PR and combined. The 3G68 products are packaged as daily files but provide hourly information at 0.5 deg x 0.5 deg resolution globally, 0.25 deg x 0.25 deg globally, or 0.1 deg x 0.1 deg over Africa, Australia and South America. This paper will present early information of the changes in the v7 TMI GPROF level 2 retrievals that have an impact on the level 3 accumulations. This paper provides an analysis of the effect the 2A12 GPROF changes have on 3G68 products. In addition, it provides a comparison between the TRMM level 3 products that use the TMI GPROF swath retrievals.

Stocker, Erich↗
NASA NTRS2012

TRMM Version 7 Level 3 Gridded Monthly Accumulations of GPROF Precipitation Retrievals

In July 2011, improved versions of the retrieval algorithms were approved for TRMM. All data starting with June 2011 are produced only with the version 7 code. At the same time, version 7 reprocessing of all TRMM mission data was started. By the end of August 2011, the 14+ years of the reprocessed mission data became available online to users. This reprocessing provided the opportunity to redo and enhance upon an analysis of V7 impacts on L3 data accumulations that was presented at the 2010 EGU General Assembly. This paper will discuss the impact of algorithm changes made in th GPROF retrieval on the Level 2 swath products. Perhaps the most important change in that retrieval was to replacement of a model based a priori database with one created from Precipitation Radar (PR) and TMI brightness temperature (Tb) data. The radar pays a major role in the V7 GPROF (GPROF2010) in determining existence of rain. The level 2 retrieval algorithm also introduced a field providing the probability of rain. This combined use of the PR has some impact on the retrievals and created areas, particularly over ocean, where many areas of low-probability precipitation are retrieved whereas in version 6, these areas contained zero rain rates. This paper will discuss how these impacts get translated to the space/time averaged monthly products that use the GPROF retrievals. The level 3 products discussed are the gridded text product 3G68 and the standard 3A12 and 3B31 products. The paper provides an overview of the changes and explanation of how the level 3 products dealt with the change in the retrieval approach. Using the .25 deg x .25 degree grid, the paper will show that agreement between the swath product and the level 3 remains very high. It will also present comparisons of V6 and V7 GPROF retrievals as seen both at the swath level and the level 3 time/space gridded accumulations. It will show that the various L3 products based on GPROF level 2 retrievals are in close agreement. The paper concludes by outlining some of the challenges of the TRMM version 7 level 3 products.

Stocker, E. F.↗
NASA NTRS2022

Evaluation of GPROF V05 Precipitation Retrievals under Different Cloud Regimes

Precipitation retrievals from passive microwave satellite observations form the basis of many widely used precipitation products, but the performance of the retrievals depends on numerous factors such as surface type and precipitation variability. Previous evaluation efforts have identified bias dependence on precipitation regime, which may reflect the influence on retrievals of recurring factors. In this study, the concept of a regime-based evaluation of precipitation from the Goddard profiling (GPROF) algorithm is extended to cloud regimes. Specifically, GPROF V05 precipitation retrievals under four different cloud regimes are evaluated against ground radars over the United States. GPROF is generally able to accurately retrieve the precipitation associated with both organized convection and less organized storms, which collectively produce a substantial fraction of global precipitation. However, precipitation from stratocumulus systems is underestimated over land and overestimated over water. Similarly, precipitation associated with trade cumulus environments is underestimated over land, while biases over water depend on the sensor’s channel configuration. By extending the evaluation to more sensors and suppressed environments, these results complement insights previously obtained from precipitation regimes, thus demonstrating the potential of cloud regimes in categorizing the global atmosphere into discrete systems.

precipitation↗
NASA NTRS2004

The Goddard Profiling Algorithm (GPROF): Description and Current Applications

Atmospheric scientists use different methods for interpreting satellite data. In the early days of satellite meteorology, the analysis of cloud pictures from satellites was primarily subjective. As computer technology improved, satellite pictures could be processed digitally, and mathematical algorithms were developed and applied to the digital images in different wavelength bands to extract information about the atmosphere in an objective way. The kind of mathematical algorithm one applies to satellite data may depend on the complexity of the physical processes that lead to the observed image, and how much information is contained in the satellite images both spatially and at different wavelengths. Imagery from satellite-borne passive microwave radiometers has limited horizontal resolution, and the observed microwave radiances are the result of complex physical processes that are not easily modeled. For this reason, a type of algorithm called a Bayesian estimation method is utilized to interpret passive microwave imagery in an objective, yet computationally efficient manner.

Olson, William S.↗
NASA NTRS2021

Extreme Lake-Effect Snow from a GPM Microwave Imager Perspective: Observational Analysis and Precipitation Retrieval Evaluation

This study focuses on the ability of the Global Precipitation Measurement (GPM) passive microwave sensors to detect and provide quantitative precipitation estimates (QPE) for extreme lake-effect snowfall events over the United States lower Great Lakes region. GPM Microwave Imager (GMI) high frequency channels can clearly detect intense shallow convective snowfall events. However, GMI Goddard PROfiling (GPROF) QPE retrievals produce inconsistent results when compared against the Multi-Radar/Multi-Sensor (MRMS) ground-based radar reference dataset. While GPROF retrievals adequately capture intense snowfall rates and spatial patterns of one event, GPROF systematically underestimates intense snowfall rates in another event. Furthermore, GPROF produces abundant light snowfall rates that do not conform with MRMS observations. Ad-hoc precipitation rate thresholds are suggested to partially mitigate GPROF’s overproduction of light snowfall rates. The sensitivity and retrieval efficiency of GPROF to key parameters (2-meter temperature, total precipitable water, and background surface type) used to constrain the GPROF a-priori retrieval database are investigated. Results demonstrate that typical lake-effect snow environmental and surface conditions, especially coastal surfaces, are underpopulated in the database and adversely affect GPROF retrievals. For the two presented case studies, using snow cover a-priori database in the locations of originally deemed as coastline improves retrieval. This study suggests that it is particularly important to have more accurate GPROF surface classifications and better representativeness of the a-priori databases to improve intense lake-effect snow detection and retrieval performance.

Lisa Milani↗
NASA NTRS1999

A Combined Infrared and Microwave Technique for Studying the Diurnal Variation of Rainfall over Amazonia

Over 10 years of continuous data from the Special Sensor microwave Imager (SSM/I) aboard a series of Defense Department satellites has made it possible to construct regional rainfall climatologies at high spatial resolution. Using the Goddard Profiling Algorithm (GPROF), monthly estimates of precipitation were made over the region of northern Brazil, including the Amazon Basin, for 1987 to 1998. GPROF is a physical approach to passive microwave precipitation retrieval, which uses the Goddard Cumulus Ensemble (cloud) model to establish prior probability densities of precipitation structures. Precipitation fields from GPROF were stratified into morning and evening satellite overpasses, and accumulated at monthly intervals at 0.5 degree spatial resolution. Important diurnal effects were noted in the analysis, the most pronounced being a land/sea breeze circulation along the northern coast of Brazil and a mountain/valley circulation along the Andes. There were also indications of morning rainfall maxima along the major rivers, and evening maxima between the rivers. The addition of simultaneous geosynchronous infrared (IR) data leads to the current technique, which takes advantage of the 30 minute sampling and 4 km spatial resolution of the infrared channel and the better physics of the microwave retrieval. The resultant IR method is subsequently used to derive the diurnal variability of rainfall over the Amazon basin, and further, to investigate the relative contribution from its convective and stratiform components.

Negri, Andrew J.↗
NASA NTRS1999

Climatic Variability of Precipitation from the Seasonal Cycle to ENSO Using GPCP's Merged Data Product and SSM/I-Based Microwave Estimates

Satellite estimates and gauge observations of precipitation are useful in understanding the water cycle, analyzing climatic variability, and validating climate models. The Global Precipitation Climatology Project (GPCP) released a community merged precipitation data set for the period July 1987 through the present, and has recently extended that data set back to 1986. One objective of this study is to use GPCP estimates to describe and quantify the seasonal variation of precipitation, with emphasis on the Asian summer monsoon. Another focus is the 1997-98 El Nino Southern Oscillation (ENSO) and associated extreme precipitation events. The summer monsoon tends to be drier than normal in El Nino ears. This was not observed for 1997 or 1998, while for 1997 the NCEP model produced the largest summer rain rates over India in years. This inconsistency will be examined. The average annual global precipitation rate is 2.7 mm day as estimated by GPCP, which is similar to values computed from long-term climatologies. From 30 deg N to 30 deg S the average precipitation rate is 2.7 mm day over land with a maximum in the annual cycle occurring in February-March, when the Amazon basin receives abundant rainfall. The average precipitation rate is 3.1 mm day over the tropical oceans, with a peak earlier in the season (November-December), corresponding with the transition from a strong Pacific Intertropical Convergence Zone (ITCZ) from June to November to a strong South Pacific Convergence Zone (SPCZ) from December to March. The seasonal evolution of C, C, the Asian summer monsoon stands out with rains in excess of 15 mm day off the coast of Burma in June. The GPROF pentad data also captures the onset of the tropical Pacific rainfall patterns associated with the 1997-98 ENSO. From February to October 1997 at least four rain-producing systems traveled from West to East in the equatorial corridor. A rapid transition from El Nino to La Nina conditions occurred in May-June 1998. GPCP and GPROF were used to construct precipitation-based ENSO indices to monitor El Ninos (EL) and La Ninas and (LI).

Curtis, Scott↗
NASA NTRS1999

Global Monitoring of Precipitation on Monthly and Shorter Time Scales Utilizing Low-Orbit and Geosynchronous Satellite Observations

A satellite-based system to monitor global precipitation on monthly and shorter time scales is described. The monitoring system is based primarily on the Global Precipitation Climatology Project (GPCP) global, monthly, 2.5 degree by 2.5 degree latitude-longitude product which utilizes precipitation estimates from low-orbit microwave sensors (SSM/I) and geosynchronous IR sensors and raingauge information over land. The low-orbit microwave 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. This globally complete, monthly product is available from January 1986 to the present, with an extension back to January 1979 underway using non-SSM/I data. The monthly GPCP merged data product described in the previous paragraph is available a few (2-4) months after the end of the month. An analysis based solely on low-orbit microwave (SSM/1) data and the Goddard Profiling (GPROF) algorithm is used to bring the global monitoring up to real time. Anomalies from climatological means are produced from both the GPCP and GPROF fields to monitor the evolution of global precipitation, including the calculation of ENSO precipitation indices for real-time (five- day running means) climate monitoring and comparison with previous ENSO anomalies. The long-term climatology of the global precipitation field and the time and space variations thereof will be discussed, including the variations associated with the 1997- 1998 ENSO. The GPCP fields will also be compared to analyses based on the recently launched Tropical Rain Measuring Mission (TRMM). On an even shorter time scale, a new daily, 1 degree x 1 degree latitude-longitude global analysis has been developed starting in January 1997 utilizing low-orbit microwave and geosynchronous IR information using a similar method as is used to produce the monthly GPCP product. Retaining the overall small bias of the monthly product the daily product will allow greater utilization in the hydrology and other science communities.

Adler, Robert↗
NASA NTRS1999

Rainfall Estimates from the TMI and the SSM/I

The Tropical Rainfall Measuring Mission (TRMM), which is a joint Japan-U.S. Earth observing satellite, has been successfully launched from Japan on November 27, 1997. The main purpose of the TRMM is to measure quantitatively rainfall over the tropics for the research of climate and weather. One of three rainfall measuring instruments abroad the TRMM is the high resolution TRMM Microwave Imager (TMI). The TMI instrument is essentially the copy of the SSM/I with a dual-polarized pair of 10.7 GHz channels added to increase the dynamic range of rainfall estimates. In addition, the 21.3 GHz water vapor absorption channel is designed in the TMI as opposed to the 22.235 GHz in the SSM/I to avoid saturation in the tropics. This paper will present instantaneous rain rates estimated from the coincident TMI and SSM/I observations. The algorithm for estimating instantaneous rainfall rates from both sensors is the Goddard Profiling algorithm (Gprof). The Gprof algorithm is a physically based, multichannel rainfall retrieval algorithm, The algorithm is very portable and can be used for various sensors with different channels and resolutions. The comparison of rain rates estimated from TMI and SSM/I on the same rain regions will be performed. The results from the comparison and the insight of tile retrieval algorithm will be given.

Hong, Ye↗
NASA NTRS2000

Retrieved Vertical Profiles of Latent Heat Release Using TRMM Rainfall Products

This paper represents the first attempt to use TRMM rainfall information to estimate the four dimensional latent heating structure over the global tropics for February 1998. The mean latent heating profiles over six oceanic regions (TOGA COARE IFA, Central Pacific, S. Pacific Convergence Zone, East Pacific, Indian Ocean and Atlantic Ocean) and three continental regions (S. America, Central Africa and Australia) are estimated and studied. The heating profiles obtained from the results of diagnostic budget studies over a broad range of geographic locations are used to provide comparisons and indirect validation for the heating algorithm estimated heating profiles. Three different latent heating algorithms, the Goddard Convective-Stratiform (CSH) heating, the Goddard Profiling (GPROF) heating, and the Hydrometeor heating (HH) are used and their results are intercompared. The horizontal distribution or patterns of latent heat release from the three different heating retrieval methods are quite similar. They all can identify the areas of major convective activity (i.e., a well defined ITCZ in the Pacific, a distinct SPCZ) in the global tropics. The magnitude of their estimated latent heating release is also not in bad agreement with each other and with those determined from diagnostic budget studies. However, the major difference among these three heating retrieval algorithms is the altitude of the maximum heating level. The CSH algorithm estimated heating profiles only show one maximum heating level, and the level varies between convective activity from various geographic locations. These features are in good agreement with diagnostic budget studies. By contrast, two maximum heating levels were found using the GPROF heating and HH algorithms. The latent heating profiles estimated from all three methods can not show cooling between active convective events. We also examined the impact of different TMI (Multi-channel Passive Microwave Sensor) and PR (Precipitation Radar) rainfall information on latent heating structures.

Tao, W.-K.↗
NASA NTRS2003

Cloud-Resolving Model and GPM

Over the past twenty years, rainfall retrieval algorithms have been developed to retrieve rainfall and vertical hydrometeor structures from passive microwave observations by making use of the fact that weighting functions for various frequencies peak at different levels within a rainy atmosphere. GPROF is one of two TMI rainfall algorithms. It is physically based retrieval that finds the vertical hydrometeor profile that best fits the brightness temperatures in the available passive radiometer channels. Matching is achieved using a library of hydrometeor profiles generated by cloud-resolving models (CRMs). The hydrometeor profiles have a corresponding surface precipitation rate. The algorithm retrieves the hydrometeor profiles and associated surface rainfall using a Bayesian approach that gives the estimated expected values. The ability of CRMs to produce cloud structures that are reliable and representative of observed storms is crucial for the success of GPROF. The cloud mycrophysics are one of the keys to achieving this. In addition, CRMs have been a very useful tool for GPM-algorithm developers through Cloud-Radiation Simulations (CRS), one of the nine GPM disciplinary research themes. This paper will discuss how to generate consistent and comprehensive 4D cloud datasets from an improved (i.e., in regard to bulk and multi-moment microphysics) CRM for TRMM and GPM rainfall retrieval algorithm developers. These cloud datasets include CRM-simulated clouds and cloud systems from different geographic locations in the tropics and midlatitudes. By linking the CRM with a passive microwave radiative-transfer model and using satellite and airborne data, the performance of the "cloud physics" can be assessed and in turn modified and improved. This paper will also address how to assess and improve the performance of various latent and diabatic heating algorithms and develop an algorithm to retrieve the vertical structure of apparent moistening (Q2). Considering that the GPM will produce high (temporal and spatial) resolution heating and rainfall data, these algorithms will be used to obtain the temporal and spatial distributions of surface rainfall and the associated vertical heating and moistening profiles throughout the subtropical and midlatitudes.

Tao, Wei-Kuo↗