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NASA NTRS2019

Integrated Multi-Satellite Evaluation for the Global Precipitation Measurement: Impact of Precipitation Types on Spaceborne Precipitation Estimation

Integrated multi-sensor assessment is proposed as a novel approach to advance satellite precipitation validation in order to provide users and algorithm developers with an assessment adequately coping with the varying performances of merged satellite precipitation estimates. Gridded precipitation rates retrieved from space sensors with quasi-global coverage feed numerous applications ranging from water budget studies to forecasting natural hazards caused by extreme events. Characterizing the error structure of satellite precipitation products is recognized as a major issue for the usefulness of these estimates. The Global Precipitation Measurement (GPM) mission aims at unifying precipitation measurements from a constellation of low-earth orbiting (LEO) sensors with various capabilities to detect, classify and quantify precipitation. They are used in combination with geostationary observations to provide gridded precipitation accumulations. The GPM Core Observatory satellite serves as a calibration reference for consistent precipitation retrieval algorithms across the constellation. The propagation of QPE uncertainty from LEO active/passive microwave (PMW) precipitation estimates to gridded QPE is addressed in this study, by focusing on the impact of precipitation typology on QPE from the Level-2 GPM Core Observatory Dual-frequency Precipitation Radar (DPR) to the Microwave Imager (GMI) to Level-3 IMERG precipitation over the Conterminous U.S. A high-resolution surface precipitation used as a consistent reference across scales is derived from the ground radar-based Multi-Radar/Multi-Sensor. While the error structure of the DPR, GMI and subsequent IMERG is complex because of the interaction of various error factors, systematic biases related to precipitation typology are consistently quantified across products. These biases display similar features across Level-2 and Level-3, highlighting the need to better resolve precipitation typology from space and the room for improvement in global-scale precipitation estimates. The integrated analysis and framework proposed herein applies more generally to precipitation estimates from sensors and error sources affecting low-earth orbiting satellites and derived gridded products.

Rainfall↗

NASA NTRS2020

Integrated Multi-satellite Evaluation for the Global Precipitation Measurement: Impact of Precipitation Types on Spaceborne Precipitation Estimation

An integrated multi-sensor assessment is proposed as a novel approach to advance satellite precipitation validation in order to provide users and algorithm developers with an assessment adequately coping with the varying performances of merged satellite precipitation estimates. Gridded precipitation rates retrieved from space sensors with quasi-global coverage feed numerous applications ranging from water budget studies to forecasting natural hazards caused by extreme events. Characterizing the error structure of satellite precipitation products is recognized as a major issue for the usefulness of these estimates. The Global Precipitation Measurement (GPM) mission aims at unifying precipitation measurements from a constellation of low-earth orbiting (LEO) sensors with various capabilities to detect, classify and quantify precipitation. They are used in combination with geostationary observations to provide gridded precipitation accumulations. The GPM Core Observatory satellite serves as a calibration reference for consistent precipitation retrieval algorithms across the constellation. The propagation of QPE uncertainty from LEO active/passive microwave (PMW) precipitation estimates to gridded QPE is addressed in this study, by focusing on the impact of precipitation typology on QPE from the Level-2 GPM Core Observatory Dual-frequency Precipitation Radar (DPR) to the Microwave Imager (GMI) to Level-3 IMERG precipitation over the Conterminous U.S. A high-resolution surface precipitation used as a consistent reference across scales is derived from the ground radar-based Multi-Radar/MultiSensor. While the error structure of the DPR, GMI and subsequent IMERG is complex because of the interaction of various error factors, systematic biases related to precipitation typology are consistently quantified across products. These biases display similar features across Level-2 and Level-3, highlighting the need to better resolve precipitation typology from space and the room for improvement in globalscale precipitation estimates. The integrated analysis and framework proposed herein applies more generally to precipitation estimates from sensors and error sources affecting low-earth orbiting satellites and derived gridded products.

Pierre-Emmanuel Kirstette↗

NASA NTRS1993

Calcified microbes in Neoproterozoic carbonates: implications for our understanding of the Proterozoic/Cambrian transition

Tidal flat and lagoonal dolostones of the Neoproterozoic Draken Formation, Spitsbergen, exhibit excellent preservation of carbonate fabrics, including heavily calcified microfossils. The crust-forming cyanobacterium Polybessurus is preserved locally by carbonate precipitated on and within sheaths in mildly evaporitic upper intertidal to supratidal environments. In contrast, calcified filaments in columnar stromatolites reflect subtidal precipitation. Filament molds in dolomicrites independently document extremely early lithification. The presence of heavily calcified cyanobacteria in Draken and other Proterozoic carbonates constrains potential explanations for the widespread appearance of calcified microorganisms near the Proterozoic-Cambrian boundary. We propose that the rarity of Proterozoic examples principally reflects the abundance and wide distribution of carbonate crystals precipitated on the sea floor or in the water column. Cyanobacterial sheaths would have competed effectively as sites for carbonate nucleation and growth only where calcitic and/or aragonitic nuclei were absent. In this view, the Proterozoic-Cambrian expansion of calcified microfossils primarily reflects the emergence of skeletons as principal agents of carbonate deposition.

Non-NASA Center↗

NASA NTRS2003

Local and Remote Sources of Precipitation during the 1993 Midwestern United States Flood

During June and July of 1993, devastating and persistent rainfall caused substantial damage in the Midwest United States primarily focused on the Mississippi River Basin. Above noma1 springtime rains primed the region for flooding. The resulting wet soil was thought to be a major source of water for the heavy summer precipitation. On the other hand, northward moisture transport was extremely intense during the summer. In this study, we have implemented passive tracers to diagnose the geographic source of water for precipitation, called Water Vapor Tracers (WVT), into the NASA Data Assimilation Office (DAO) Data Assimilation System (DAS). The WVT diagnostics can produce detailed budgets of the geographical sources of water that precipitates in any region, and they consider physical and dynamical tendencies at the model time step. The 1993 summer has been reanalyzed with the new WVT diagnostics to better understand the local and remote source of water for precipitation during the flood. Because the DAS also uses observations of water in the atmospheric hydrologic budget, we will also evaluate the impact of the observations on the local and remote sources of water in the DAS.

Bosilovich, Michael↗

NASA NTRS2007

The ENSO Effect on the Temporal and Spatial Distribution of Global Lightning Activity

The recently reprocessed (1997-2006) OTD/LIS database is used to investigate the global lightning climatology in response to the ENSO cycle. A linear correlation map between lightning anomalies and ENSO (NINO3.4) identifies areas that generally follow patterns similar to precipitation anomalies. We also observed areas where significant lightning/ENSO correlations are found and are not accompanied of significant precipitation/ENSO correlations. An extreme case of the strong decoupling between lightning and precipitation is observed over the Indonesian peninsula (Sumatra) where positive lightning/NINO3.4 correlations are collocated with negative precipitation/NINO3.4 correlations. Evidence of linear relationships between the spatial extent of thunderstorm distribution and the respective NINO3.4 magnitude are presented for different regions on the Earth. Strong coupling is found over areas remote to the main ENSO axis of influence and both during warm and cold ENSO phases. Most of the resulted relationships agree with the tendencies of precipitation related to ENSO empirical maps or documented teleconnection patterns. Over the Australian continent, opposite behavior in terms of thunderstorm activity is noted for warm ENSO phases with NINO3.4 magnitudes with NINO3.4>+l.08 and 0<NqNO3.4<I.08. Finally, we investigate the spatial distribution of areas that consistently portrayed enhanced lightning activity during the main warm/cold (El Nino/La Nina) ENSO episodes of the past decade. The observed patterns show no spatial overlapping and identify areas that in their majority are in agreement with empirical precipitation/ENSO maps. The areas that appear during the warm ENSO phase are found over regions that have been identified as anomalous Hadley circulation ENSO-related patterns. The areas that appear during the cold ENSO phase are found predominantly around the west hemisphere equatorial belt and are in their majority identified by anomalous Walker circulation.

Chronis, Themis G.↗

NASA NTRSDate not supplied

Applied Uses of MERRA Reanalyses: Current and Future Prospects

During the initial planning of the Modern Era Retrospective-analysis for Research and Applications (MERRA), the project was envisioned to become a tool for applied sciences and decision makers, as well as weather and climate research. MERRA was the first reanalysis to provide data at 1 hourly frequencies. After discussions with wind energy users, 50m above the surface winds were included in the data collection to begin to represent the environment at the turbine height. MERRA-2 was the first reanalysis to include interactive and assimilated aerosols. From these, PM 1, 2.5 and 10 records have been computed and contribute to the climate and health sector. The US Center for Disease Control is accessing MERRA-2 data for comparison along with their health data. MERRA-2 Extreme Indices have been computed from the high frequency data (e.g. precipitation maxima, heat waves and fire weather). These have been used to characterize the changing extremes in the United States and around the globe as well as the weather associated with the extremes. The next-generation, MERRA-21C, includes increased resolution (25km) and continues to provide innovative Earth system data. For example, a boundary layer collection will provide constant height level data starting at 100m, continuing up to 4000m. This will provide detailed information about the boundary layer processes that greatly affect the biosphere. Chemical tracers for constituents are included in the system. The ensemble data assimilation is run with 32 members, and the variance for critical variables are stored. We will explore the use of this variance as one measure of uncertainty relevant for applied uses, which has not been included in any MERRA reanalysis. We will discuss use case scenarios developed to engage with the energy and financial sectors, to better understand their needs for climate data. Prospects for further downscaling of reanalyses will also be explored.

Michael Bosilovich↗

NASA NTRSDate not supplied

Warming Reduces the Efficacy of Wet Conditions to Moderate Extreme Heat and Atmospheric Aridity Across the Central Plains

In the literature, dry land surface conditions possess a well-established association with extreme heat and high atmospheric aridity, especially in regions that experience strong land-atmosphere feedbacks. However, few studies have examined whether global warming will impact the ability of wet conditions to constrain high temperatures and atmospheric aridity occurrences. Here we investigate how wet conditions, which we define using precipitation and soil moisture, constrain high temperature and high vapor pressure deficit during the warm season over the Central Plains in North America at different global warming levels (0-3 degrees K). Using a CMIP6 multi-model ensemble, we show that absolute changes in the maximum temperature distribution outweigh the historical cooling effect of high precipitation or soil moisture conditions, reducing the probability of non-extreme maximum temperatures under global warming. Strikingly, the conditional likelihood of non-extreme maximum temperatures falls from 97-98% at baseline conditions to 50-58% at 1 degree of global warming. Once 2 degrees of warming is reached, there is very little probability of constraining extreme maximum temperatures. On the other hand, high precipitation and high soil moisture are able to better retain the ability to constrain concurrent high vapor pressure deficit conditions – moving from 99% at baseline conditions to 81-91% at 1 degree of warming and 26-42% at 2 degrees of warming. With our results, we find that the extent of this retention is dependent on how individual models represent relative changes in the VPD distribution as temperatures warm. Overall, our results highlight the growing vulnerability of the Central North American region to warmer temperatures and drier atmospheric conditions, even during periods of high precipitation and soil moisture.

Extreme heat↗

NASA NTRS2005

Land surface water cycles observed with satellite sensors

Acceleration of the global water cycle may lead to increased global precipitation, faster evaporation and a consequent exacerbation of hydrologic extreme. In the U.S. national assessment of the potential consequences of climate variability and change, two GCMs (CGCM1 and HadCM2) show a large increase in precipitation in the future over the southwestern U.S. particularly during winter (Felzer and Heard, 1999). Increased precipitation potentially has important impacts on agricultural and water use in the southeast U.S. (Hatch et al., 1999) and in the central Great Plains (Nielsen, 1997). A hurricane model predicts a 40% precipitation increase for severe hurricanes affecting southeastern Florida, which provokes substantially greater flooding that could negate most of the benefits of present water-management practices in this basin (Gutowski et al., 1994). Thus, it is important to observe the hydroclimate on a continuous longterm basis to address the question of increased precipitation in the enhanced water cycle.

droughts↗

NASA NTRS1991

Thermal stability of the microstructure of an aged Nb-Zr-C alloy

The effects of thermal aging with and without an applied stress on the microstructure of a Nb-Zr-C alloy containing 0.9 wt percent Zr and 0.06 percent C were studied. Chemical analysis, metallographic examination, energy dispersive X-ray spectra of the bulk material, and chemical and X-ray analyses of the phase-extracted residue were used to characterize the microstructure. The samples examined were from a creep strength study involving hot and cold working, and various combinations of exposure to temperatures ranging from 1350 to 1755 K with and without applied load times as long as 34,000 plus hours. The results showed that the initial microstructure consisted primarily of orthorombic precipitates of Nb sub C which were partially or completely transformed to face-centered cubic carbides of Nb and Zr, (Zr, Nb)C, upon prolonged exposure to elevated temperatures. Furthermore, it was found that the microstructure of the alloy is extremely stable owing to the very finely distributed precipitates throughout its matrix and along the grain boundaries. The lattice parameters of the cubic carbides were determined to vary from 0.458 to 0.465 nm as the Zr/Nb ratio varied from 38/62 to 75/25.

Uz, Mehmet↗

NASA NTRS1990

Thermal stability of the microstructure of an aged Nb-Zr-C alloy

The effects of thermal aging with and without an applied stress on the microstructure of a Nb-Zr-C alloy containing 0.9 wt percent Zr and 0.06 wt percent C were studied. Chemical analysis, metallographic examination, energy dispersive x-ray spectra of the bulk material, and chemical and x-ray analyses of the phase-extracted residue were used to characterize the microstructure. The samples examined were from a creep strength study involving hot and cold working, and various combinations of exposure to temperatures ranging from 1350 to 1755 K with and without applied load for times as long as 34,000 plus hours. The results showed that the initial microstructure consisted primarily of orthorombic precipitates of Nb sub 2 C which were partially or completely transformed to face-centered cubic carbides of nb and Zr, (Zr, Nb)C, upon prolonged exposure to elevated temperatures. Furthermore, it was found that the microstructure of the alloy is extremely stable owing to the very finely distributed precipitates throughout its matrix and along the grain boundaries. The lattice parameters of the cubic carbides were determed to vary from 0.458 to 0.465 nm as the Zr/Nb ratio varied from 38/62 to 75/25.

Uz, Mehmet↗

NASA NTRS2005

Energetic Electron Transport in the Inner Magnetosphere During Geomagnetic Storms and Substorms

We propose to examine the relationship of geomagnetic storms and substorms and the transport of energetic particles in the inner magnetosphere using measurements of the auroral X-ray emissions by PIXIE. PIXIE provides a global view of the auroral oval for the extended periods of time required to study stormtime phenomena. Its unique energy response and global view allow separation of stormtime particle transport driven by strong magnetospheric electric fields from substorm particle transport driven by magnetic-field dipolarization and subsequent particle injection. The relative importance of substorms in releasing stored magnetospheric energy during storms and injecting particles into the inner magnetosphere and the ring current is currently hotly debated. The distribution of particles in the inner magnetosphere is often inferred from measurements of the precipitating auroral particles. Thus, the global distributions of the characteristics of energetic precipitating particles during storms and substorms are extremely important inputs to any description or model of the geospace environment and the Sun-Earth connection. We propose to use PIXIE observations and modeling of the transport of energetic electrons to examine the relationship between storms and substorms.

McKenzie, D. L.↗

NASA NTRS2011

Major Characteristics of Southern Ocean Cloud Regimes and Their Effects on the Energy Budget

Clouds over the Southern Ocean are often poorly represented by climate models, but they make a significant contribution to the top-of-atmosphere (TOA) radiation balance, particularly in the shortwave portion of the energy spectrum. This study seeks to better quantify the organization and structure of Southern Hemisphere midlatitude clouds by combining measurements from active and passive satellite-based datasets. Geostationary and polar-orbiter satellite data from the International Satellite Cloud Climatology Project (ISCCP) are used to quantify large-scale, recurring modes of cloudiness, and active observations from CloudSat and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) are used to examine vertical structure, radiative heating rates, and precipitation associated with these clouds. It is found that cloud systems are organized into eight distinct regimes and that ISCCP overestimates the midlevel cloudiness of these regimes. All regimes contain a relatively high occurrence of low cloud, with 79%of all cloud layers observed having tops below 3 km, but multiple-layered clouds systems are present in approximately 34% of observed cloud profiles. The spatial distribution of regimes varies according to season, with cloud systems being geometrically thicker, on average, during the austral winter. Those regimes found to be most closely associated with midlatitude cyclones produce precipitation the most frequently, although drizzle is extremely common in low-cloud regimes. The regimes associated with cyclones have the highest in-regime shortwave cloud radiative effect at the TOA, but the low-cloud regimes, by virtue of their high frequency of occurrence over the oceans, dominate both TOA and surface shortwave effects in this region as a whole.

Haynes, John M.↗

NASA NTRS2013

Ground Water and Climate Change

As the world's largest distributed store of fresh water, ground water plays a central part in sustaining ecosystems and enabling human adaptation to climate variability and change. The strategic importance of ground water for global water and food security will probably intensify under climate change as more frequent and intense climate extremes (droughts and floods) increase variability in precipitation, soil moisture and surface water. Here we critically review recent research assessing the impacts of climate on ground water through natural and human-induced processes as well as through groundwater-driven feedbacks on the climate system. Furthermore, we examine the possible opportunities and challenges of using and sustaining groundwater resources in climate adaptation strategies, and highlight the lack of groundwater observations, which, at present, limits our understanding of the dynamic relationship between ground water and climate.

climate change↗

NASA NTRSDate not supplied

Active Solar-Like Stars and the Production of Prebiotically Relevant Molecules in Young Earth Like Exoplanet

The magnetic activity of young G type stars produces elevated ionizing radiation fluxes for its planetary system. X-ray, Extreme UV, flare radiation and the associated precipitating energetic particles accelerated in coronal mass ejection driven shocks can drive complex chemistry in the exoplanetary atmosphere, and ignite the production of complex molecules (Airapetian et al., 2016; Hayworth et al. 2021). To address the efficiency of the production of prebiotic molecules in young Earth-like environments, we developed and coupled a set of atmospheric models that account for the determination of the flux of precipitating particle, its ionization/dissociation effects in the planetary atmosphere, and the subsequent chemistry that leads to the creation of prebiotic molecules and greenhouse gases. We show that the productions rates are high enough to provide critical input for the origin of prebiotic molecules on the early Earth. Overall, the impact of the space weather on the atmospheric photochemistry should not be neglected when assessing the factors of habitability of the environments of young rocky (exo)planets.

Guillaume Gronoff↗

NASA NTRSDate not supplied

A Knowledge Graph Framework for Organizing Heterogeneous Datasets for Utilization in Classical and Quantum Computing: Current Challenges and Future Directions

"The escalating impact of climate change induced extreme weather events in urban, suburban, and rural environments demands a rethink of how we have been using the single event-based or use-case-based knowledge graph models. The lack of representation in interaction within environmental variables found in literature led to the development of a novel framework that reflects the true nature of the interconnectedness in our environment. We propose an Environmental Interaction Knowledge Graph (EIKG) framework. This general EIKG framework works as the basis for interconnected environmental events by knitting interrelated events such as hurricanes leading to storm surges, which lead to flood events that could cause mudslides, landslides, etc., The cascading nature of one event leading to another related event in the environment requires an adequate understanding of each event using contextual information before conducting any data-driven analytics. This vision paper showcases how the EIKG:floods, EIKG:wildfire EIKG:landslides, etc, can be derived from a base case framework of EIKG as those individual events are interconnected with some common denominator variables. As an example, the precipitation variable is used in the flood case study as well as in the wildfire case study, as excessive precipitation levels lead to floods, and lack of precipitation leads to droughts and wildfires. We identify the precipitation variable as a “common-denominator-variable” in extreme weather events that play a key role in modeling the environment leading to different extreme weather events based on the variability of that variable (varying values where low precipitation leads to drought, and high values lead to floods). We use the insights gained from EIKG to conduct classical and Quantum Machine Learning (QML) based data analysis on the research questions developed. Our preliminary study shows how the Variational Quantum Classifier (VQC) and Quantum Support Vector Classifier (QSVC) are used along with the classical machine learning models to compare the model accuracies. Our study elaborates on how a quantitative analysis uses state-of-the-art machine learning techniques that include implementing both classical and quantum machine learning models and developing the knowledge graph. The EIKG is used to organize heterogeneous datasets and integrate the relations to case-specific extreme weather events such as floods. The study uses datasets such as county-to-country residential mobility data, socioeconomic datasets from the US Census Bureau, climate and weather-related Earth Observational data from NASA, and critical infrastructure data from the Homeland Infrastructure datasets."

Knowledge Graphs, Quantum Computing, Heterogenous ↗

NASA NTRS2013

Northern Eurasian Heat Waves and Droughts

This article reviews our understanding of the characteristics and causes of northern Eurasian summertime heat waves and droughts. Additional insights into the nature of temperature and precipitation variability in Eurasia on monthly to decadal time scales and into the causes and predictability of the most extreme events are gained from the latest generation of reanalyses and from supplemental simulations with the NASA GEOS-5 AGCM. Key new results are: 1) the identification of the important role of summertime stationary Rossby waves in the development of the leading patterns of monthly Eurasian surface temperature and precipitation variability (including the development of extreme events such as the 2010 Russian heat wave), 2) an assessment of the mean temperature and precipitation changes that have occurred over northern Eurasia in the last three decades and their connections to decadal variability and global trends in SST, and 3) the quantification (via a case study) of the predictability of the most extreme simulated heat wave/drought events, with some focus on the role of soil moisture in the development and maintenance of such events. A literature survey indicates a general consensus that the future holds an enhanced probability of heat waves across northern Eurasia, while there is less agreement regarding future drought, reflecting a greater uncertainty in soil moisture and precipitation projections. Substantial uncertainties remain in our understanding of heat waves and drought, including the nature of the interactions between the short-term atmospheric variability associated with such extremes and the longer-term variability and trends associated with soil moisture feedbacks, SST anomalies, and an overall warming world.

SST↗

NASA NTRS2011

AIRS Impact on Analysis and Forecast of an Extreme Rainfall Event (Indus River Valley 2010) with a Global Data Assimilation and Forecast System

A set of data assimilation and forecast experiments are performed with the NASA Global data assimilation and forecast system GEOS-5, to compare the impact of different approaches towards assimilation of Advanced Infrared Spectrometer (AIRS) data on the precipitation analysis and forecast skill. The event chosen is an extreme rainfall episode which occurred in late July 11 2010 in Pakistan, causing massive floods along the Indus River Valley. Results show that the assimilation of quality-controlled AIRS temperature retrievals obtained under partly cloudy conditions produce better precipitation analyses, and substantially better 7-day forecasts, than assimilation of clear-sky radiances. The improvement of precipitation forecast skill up to 7 day is very significant in the tropics, and is caused by an improved representation, attributed to cloudy retrieval assimilation, of two contributing mechanisms: the low-level moisture advection, and the concentration of moisture over the area in the days preceding the precipitation peak.

Reale, O.↗

NASA NTRSDate not supplied

Insights into Long-Term Global Precipitation from IMERG and GPCP

Multiple satellite-based datasets provide estimates of the long-term record of global precipitation. Each necessarily includes both the real atmospheric behavior and a collection of artifacts driven by the input data sources and design choices in the retrievals and dataset construction. For this presentation, the Integrated Multi-satellitE Retrievals for GPM (IMERG) products from the Global Precipitation Measurement (GPM) mission’s U.S. Science Team are designed as a modern High-Resolution Precipitation Product (HRPP), while the Global Precipitation Climatology Project’s (GPCP) products are designed as a modern Climate Data Record (CDR). Although they share some common input data sources, the computational process for each is rather different. Comparing the precipitation estimates from each algorithm gives us insights into likely artifacts and the natural variations that are common to both. The goal of an HRPP is to give the best estimate of precipitation at each time step in the dataset, generally meaning that “all possible available” data are used. In contrast, the CDR is intended to provide a precipitation record that has relatively homogeneous statistics, necessary for climate analysis. Each analysis strives for both goals, of course, but this means that the HRPP uses the disparate collection of satellites whose statistics don’t quite match the ideal record, while the CDR is computed from fewer satellites that provide a relatively homogeneous set of precipitation statistics. This presentation employs the current versions of the IMERG and GPCP products, V07B and V3.2, respectively. A variety of analyses have demonstrated that IMERG has more artifacts, as expected, which tend to result from changes in the source of intercalibration for the various input satellites, namely the Combined Radar-Radiometer Algorithm using TMI and PR during the TRMM era and using GMI and DPR-Ku during the GPM era. As well, a shift occurs when the altitudes of the TRMM and GPM satellites change as a result of orbit boosts. For the most part the mean precipitation shows good continuity across these boundaries, but there are noticeable changes in the respective histograms. The latter has taken on more importance in recent years due to increased scrutiny on extremes which implicitly focus on the upper end of the precipitation histograms. The GPCP product is more homogeneous, with only one major change in calibrator, transitioning from the SSMI series of satellites to the SSMIS series of satellites in 2009. The GPCP analysis does a good job of minimizing artifacts in the means at this boundary, while the histograms show a smaller, but still noticeable shift in the histograms, a result that is similar to data boundaries in IMERG. The GPCP Daily, which is a month-by-month rescaling of the daily accumulated IMERG Final product, shows more consistency than the equivalent daily IMERG, but inherits the histogram shifts from IMERG. Given this overlay of artifacts, there is enough consistency between the products to illustrate some important long-term variations, including interannual variations, not all of which are easily attributable to ENSO events, and trends that are large regionally, but comparatively small when averaged across the globe.

precipitation↗