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NASA NTRS2020

The Precipitation Rate Retrieval Algorithms for the GPM Dual-frequency Precipitation Radar

In this paper, precipitation rate retrieval algorithms for the Global Precipitation Measurement mission's Dual-frequency Precipitation Radar (DPR) are developed. The DPR consists of a Ku-band radar (KuPR; 13.6 GHz) and a Ka-band radar (KaPR; 35.5 GHz). For the KuPR, an algorithm similar to the Tropical Rainfall Measuring Mission's Precipitation Radar algorithm is developed, with the relation between precipitation rate R and massweighted mean diameter D m (R−D m relation) replacing the relation between the specific attenuation k and effective radar reflectivity factor Z e . The R−D m relation can also be applied to the KaPR and dual-frequency algorithms. In both the single-frequency and dual-frequency algorithms, the forward retrieval method is applied with an assumed adjustment factor for the R−D m relation (ε) and the results are evaluated to select the best value of ε. The advantages of the dual-frequency algorithm are the availability of the dual-frequency surface reference technique and the ZfKa method, which is a method to use the attenuation-corrected radar reflectivity factor Z f of KaPR, to select ε as well as the possibility to selectively use measurements from KuPR or KaPR. This paper also describes the derivation of the scattering table and the R−D m relation as well as the procedure for non-uniform beam filling correction in detail. The outputs are then statistically analyzed to demonstrate algorithm performance.

precipitation radar↗
NASA NTRS2021

Retrieval of Snow Water Equivalent by the Precipitation Imaging Package (PIP) over Northern Great Lakes

Performance of the Precipitation Imaging Package (PIP) for estimating the snow water equivalent (SWE) is evaluated through a comparative study with the collocated National Oceanic and Atmospheric Administration National Weather Service snow stake field measurements. The PIP together with a vertically pointing radar, a weighing bucket gauge, and a laser-optical disdrometer was deployed at the NWS Marquette, Michigan office building for a long-term field study supported by the National Aeronautics and Space Administration’s Global Precipitation Measurement mission Ground Validation program. The site was also equipped with a weather station. During the 2017-18 winter, PIP functioned nearly uninterrupted at frigid temperatures accumulating 2345.8 mm of geometric snow depth over a total of 499 hours. This long record consists of 30 events, and the PIP-retrieved and snow stake field measured SWE differed less than 15% in every event. For the windy events, the wind corrected stake field record agreed better with the PIP-retrieved SWE. Two of the major events with the longest duration and the highest accumulation are examined in detail. The particle mass with a given diameter was much lower during a shallow, colder, uniform lake-effect event than in the deep, less cold, and variable synoptic event. This study demonstrated that PIP is a robust instrument for operational use, and is reliable for deriving the bulk properties of falling snow.

Ali Tokay↗
NASA NTRS2021

Evaluation of Rainfall-Snowfall Separation Performance in Remote Sensing Datasets

The first step to accurately measure global snowfall is to separate rainfall from snowfall correctly (i.e., precipitation phase discrimination). This study first evaluates the phase discrimination performance in four remote sensing datasets, including observations from ground radar, spaceborne radars, and spaceborne radiometer, relative to ground observations. Results show that the snowfall discrimination accuracy varies greatly among these datasets ranging from 42% to 96%, dependent on whether and how the temperature information are considered. For example, over half of the snowfall from the Global Precipitation Measurement Mission (GPM) spaceborne radar is actually rainfall at the surface since it detects snowfall in the air without considering the temperature information close to the surface. Second, we evaluate the discrimination performance using the temperature information from four reanalysis datasets. It is found that MERRA2 temperature close to the surface is colder than the other three datasets, leading to more rainfall being misclassified as snowfall.

Yalei You↗
NASA NTRS2023

Aconcagua Basin Agriculture: Analyzing Hydrological Norms, Evapotranspiration, and Soil Moisture to Assess Crop Water Demand and Water Usage in Chile’s Aconcagua River Basin

The Aconcagua basin has experienced a prolonged drought since 2010, posing a significant concern to the agricultural sector, which contributes to 12% of the national agriculture output. Reduced precipitation and warmer conditions have led to water constraints for agricultural activities. As the agriculture sector relies more on irrigation, there has been a decline in surface water availability and a shrinking groundwater supply, both primarily sourced from snow and glacial melt. This study focuses on evaluating crop water demand and water usage in the Aconcagua Basin, leveraging in-situ and available satellite data from Aqua and Terra Moderate Resolution Imaging Spectroradiometer (MODIS), 1-km downscaled Soil Moisture Active Passive (SMAP), Global Precipitation Measurement Mission IMERG (GPM), and Landsat 8 Operational Land Imager (OLI). Remote sensing hydrologic norms from pre- and intra-drought conditions provided a baseline for our water use analysis. We have identified areas of the basin where water usage is exaggerated compared to other regions by analyzing soil moisture, evapotranspiration, and vegetation index trends across agricultural lands. The study highlights the presence of irrigation and exaggerated water usage categorized by crop type while normalizing the data by the quantity of water allocation (m3/s) to each subregion. The findings have the potential to assist our partners, the Centro de Información de Recursos Naturales (CIREN) and the Ministry of Agriculture, in refining water allocation approaches; showcasing the feasibility of leveraging remote sensing and earth observation datasets to monitor agricultural practices within the context of water scarcity.

Remote sensing↗
NASA NTRS2024

Using Satellite Observations of Lightning and Precipitation to Diagnose the Behavior of Deep Convection in Tropical Cyclones Traversing the Midlatitudes

This study uses a unique combination of geostationary and low-Earth orbiting satellite-based lightning and precipitation observations, respectively, to examine the evolution of deep convection during the tropical cyclone (TC) lifecycle. The study spans the 2018-2021 Atlantic Basin hurricane seasons and is unique as it provides the first known analysis of total lightning (intra-cloud and cloud-to-ground) observed in TCs through their extratropical transition and post-tropical cyclone (PTC) phases. We consider the TC lifecycle stage, geographic location (e.g., land, coast, and ocean), shear strength, and quadrant relative to the storm motion and environmental shear vectors. Total lightning maxima are found in the forward right quadrant relative to storm motion and downshear of the TC center, consistent with previous studies using mainly cloud-to-ground lightning. Increasing environmental shear focuses the lightning maxima to the downshear right quadrant with respect to the shear vector in tropical storm phases. Vertical profiles of radar reflectivity from the Global Precipitation Measurement mission show that super electrically active convective precipitation features (>75 flashes) within the PTC phase of TCs have deeper mixed phase depths and higher reflectivity at -10°C than other phases, indicating the presence of more intense convection. Differences in the net convective behavior observed throughout TC evolution manifest in both the TC-scale frequency of lightning-producing cells and the intensity variations amongst individual convective cells. The combination of continuous lightning observations and precipitation snapshots improves our understanding of convective scale processes in TCs, especially in PTC phases, as they traverse the tropics and mid-latitudes.

Lightning↗
NASA NTRSDate not supplied

The Land Surface in Current and Planned MERRA Reanalysis Products

Current global atmospheric reanalysis products such as the European Centre for Medium-Range Weather Forecasts Reanalysis version 5 (ERA5), the NASA Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2), and the Japanese Reanalysis for Three Quarters of a Century (JRA-3Q) provide estimates of land surface states and fluxes, including soil moisture, soil temperature, snow mass, latent and sensible heat fluxes, and runoff, that are widely used in research and applications. These land surface estimates are based on land surface process models and, depending on the reanalysis product, on precipitation observations or the assimilation of land surface observations of soil moisture, soil temperature, snow conditions, and screen-level air temperature and humidity from satellite observations and in situ measurements. In this presentation, we review the land surface modeling and data assimilation components of the suite of current and planned MERRA reanalysis products. In addition to MERRA-2, we will discuss the latest NASA reanalysis, MERRA for the 21st century (M21C), which is currently under production, as well as the development and planning of the next version of the MERRA reanalysis, tentatively labeled MERRA-3. In MERRA-2, observations-based precipitation data products are used to correct the precipitation falling on the land surface. Outside of the high-latitudes and Africa, the daily, 0.5-degree, gauge-based Climate Prediction Center (CPC) Unified (CPCU) product is used. In Africa, the pentad, 2.5-degree, satellite- and gauge-based CPC Merged Analysis of Precipitation (CMAP) product is used. Poleward of 62.5 degrees latitude, the land surface sees the precipitation generated by the atmospheric model in the cycling data assimilation system. This configuration provides improved soil moisture estimates compared to those of the original (version 1) MERRA estimates, which did not benefit from the use of precipitation observations. Moreover, the use of precipitation observations facilitates a seamless spin-up of the land surface initial conditions across the MERRA-2 production streams. The use of a gauge-only precipitation product in MERRA-2 across much of the globe, however, adversely impacts the quality of the MERRA-2 land surface estimates in regions with poor gauge coverage, including most of South America and Australia. Therefore, the forthcoming M21C reanalysis uses satellite- and gauge-based precipitation from the Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement Mission (IMERG). This change results in significant improvements in the quality of the M21C soil moisture estimates in the Southern Hemisphere compared to those from MERRA-2. Planning for MERRA-3 focuses on the assimilation of soil moisture observations from the Soil Moisture Active Passive (SMAP) mission and the Advanced Scatterometer (ASCAT), along with snow cover area fraction observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) to further improve the quality of the land surface estimates from the reanalysis. As a first step towards the assimilation of land surface observations in MERRA-3, the offline (land-only) M21C-Land reanalysis is currently under development as a supplemental M21C product that includes the assimilation of SMAP, ASCAT, and MODIS observations. Preliminary results from M21C and M21C-Land will be discussed in the context of MERRA-2 and plans for MERRA-3.

Rolf Reichle↗
NASA NTRS2013

Downscaling Satellite Precipitation with Emphasis on Extremes: A Variational 1-Norm Regularization in the Derivative Domain

The increasing availability of precipitation observations from space, e.g., from the Tropical Rainfall Measuring Mission (TRMM) and the forthcoming Global Precipitation Measuring (GPM) Mission, has fueled renewed interest in developing frameworks for downscaling and multi-sensor data fusion that can handle large data sets in computationally efficient ways while optimally reproducing desired properties of the underlying rainfall fields. Of special interest is the reproduction of extreme precipitation intensities and gradients, as these are directly relevant to hazard prediction. In this paper, we present a new formalism for downscaling satellite precipitation observations, which explicitly allows for the preservation of some key geometrical and statistical properties of spatial precipitation. These include sharp intensity gradients (due to high-intensity regions embedded within lower-intensity areas), coherent spatial structures (due to regions of slowly varying rainfall),and thicker-than-Gaussian tails of precipitation gradients and intensities. Specifically, we pose the downscaling problem as a discrete inverse problem and solve it via a regularized variational approach (variational downscaling) where the regularization term is selected to impose the desired smoothness in the solution while allowing for some steep gradients(called 1-norm or total variation regularization). We demonstrate the duality between this geometrically inspired solution and its Bayesian statistical interpretation, which is equivalent to assuming a Laplace prior distribution for the precipitation intensities in the derivative (wavelet) space. When the observation operator is not known, we discuss the effect of its misspecification and explore a previously proposed dictionary-based sparse inverse downscaling methodology to indirectly learn the observation operator from a database of coincidental high- and low-resolution observations. The proposed method and ideas are illustrated in case studies featuring the downscaling of a hurricane precipitation field.

Hurricanes↗
NASA NTRS2001

New Approaches For Validating Satellite Global Precipitation Measurements

The scientific successes of the Tropical Rainfall Measuring Mission (TRMM) and additional recent satellite-focused precipitation retrieval projects, particularly those based on use of passive microwave radiometer measurements, have paved the way for a more advanced mission currently under development as the Global Precipitation Measurement (GPM) mission. This new mission is motivated by a number of scientific questions that TRMM research has posed over a range of space-time scales and within a variety of scientific disciplines that are becoming more integrated into earth system science modeling.

Smith, Eric A.↗
NASA NTRS2002

Global Precipitation Measurement: Benefits of Partnering with GPM Mission - Report 2

An important goal of the Global Precipitation Measurement (GPM) mission is to maximize participation by non-NASA partners both domestic and international. A consequence of this objective is the provision for NASA to provide sufficient incentives to achieve partner buy-in and commitment to the program. NASA has identified seven specific areas in which substantive incentives will be offered: (1) partners will be offered participation in governance of GPM mission science affairs including definition of data products; (2) partners will be offered use of NASA's TDRSS capability for uplink and downlink of commands and data in regards to partner provided spacecraft; (3) partners will be offered launch support for placing partner provided spacecraft in orbit conditional upon mutually agreeable co-manifest arrangements; (4) partners will be offered direct data access at the NASA-GPM server level rather than through standard data distribution channels; (5) partners will be offered the opportunity to serve as regional data archive and distribution centers for standard GPM data products; and (6) partners will be offered the option to insert their own specialized filtering and extraction software into the GPM data processing stream or to obtain specialized subsets and products over specific areas of interest (7) partners will be offered GPM developed software tools that can be run on their platforms. Each of these incentives, either individually or in combination, represents a significant advantage to partners who may wish to participate in the GPM mission.

Stocker, Erich F.↗
NASA NTRS2010

Transfer of Satellite Rainfall Uncertainty from Gauged to Ungauged Regions at Regional and Seasonal Timescales

Hydrologists and other users need to know the uncertainty of the satellite rainfall data sets across the range of time/space scales over the whole domain of the data set. Here, uncertainty' refers to the general concept of the deviation' of an estimate from the reference (or ground truth) where the deviation may be defined in multiple ways. This uncertainty information can provide insight to the user on the realistic limits of utility, such as hydrologic predictability, that can be achieved with these satellite rainfall data sets. However, satellite rainfall uncertainty estimation requires ground validation (GV) precipitation data. On the other hand, satellite data will be most useful over regions that lack GV data, for example developing countries. This paper addresses the open issues for developing an appropriate uncertainty transfer scheme that can routinely estimate various uncertainty metrics across the globe by leveraging a combination of spatially-dense GV data and temporally sparse surrogate (or proxy) GV data, such as the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar and the Global Precipitation Measurement (GPM) mission Dual-Frequency Precipitation Radar. The TRMM Multi-satellite Precipitation Analysis (TMPA) products over the US spanning a record of 6 years are used as a representative example of satellite rainfall. It is shown that there exists a quantifiable spatial structure in the uncertainty of satellite data for spatial interpolation. Probabilistic analysis of sampling offered by the existing constellation of passive microwave sensors indicate that transfer of uncertainty for hydrologic applications may be effective at daily time scales or higher during the GPM era. Finally, a commonly used spatial interpolation technique (kriging), that leverages the spatial correlation of estimation uncertainty, is assessed at climatologic, seasonal, monthly and weekly timescales. It is found that the effectiveness of kriging is sensitive to the type of uncertainty metric, time scale of transfer and the density of GV data within the transfer domain. Transfer accuracy is lowest at weekly timescales with the error doubling from monthly to weekly.However, at very low GV data density (<20% of the domain), the transfer accuracy is too low to show any distinction as a function of the timescale of transfer.

Tang, Ling↗
NASA NTRS2011

Bridging Empirical and Physical Approaches for Landslide Monitoring and Early Warning

Rainfall-triggered landslides typically occur and are evaluated at local scales, using slope-stability models to calculate coincident changes in driving and resisting forces at the hillslope level in order to anticipate slope failures. Over larger areas, detailed high resolution landslide modeling is often infeasible due to difficulties in quantifying the complex interaction between rainfall infiltration and surface materials as well as the dearth of available in situ soil and rainfall estimates and accurate landslide validation data. This presentation will discuss how satellite precipitation and surface information can be applied within a landslide hazard assessment framework to improve landslide monitoring and early warning by considering two disparate approaches to landslide hazard assessment: an empirical landslide forecasting algorithm and a physical slope-stability model. The goal of this research is to advance near real-time landslide hazard assessment and early warning at larger spatial scales. This is done by employing high resolution surface and precipitation information within a probabilistic framework to provide more physically-based grounding to empirical landslide triggering thresholds. The empirical landslide forecasting tool, running in near real-time at http://trmm.nasa.gov, considers potential landslide activity at the global scale and relies on Tropical Rainfall Measuring Mission (TRMM) precipitation data and surface products to provide a near real-time picture of where landslides may be triggered. The physical approach considers how rainfall infiltration on a hillslope affects the in situ hydro-mechanical processes that may lead to slope failure. Evaluation of these empirical and physical approaches are performed within the Land Information System (LIS), a high performance land surface model processing and data assimilation system developed within the Hydrological Sciences Branch at NASA's Goddard Space Flight Center. LIS provides the capabilities to quantify uncertainty from model inputs and calculate probabilistic estimates for slope failures. Results indicate that remote sensing data can provide many of the spatiotemporal requirements for accurate landslide monitoring and early warning; however, higher resolution precipitation inputs will help to better identify small-scale precipitation forcings that contribute to significant landslide triggering. Future missions, such as the Global Precipitation Measurement (GPM) mission will provide more frequent and extensive estimates of precipitation at the global scale, which will serve as key inputs to significantly advance the accuracy of landslide hazard assessment, particularly over larger spatial scales.

Kirschbaum, Dalia↗
NASA NTRS2002

Advances In Understanding Global Water Cycle With Advent of GPM Mission

During the coming decade, the internationally organized Global Precipitation Measurement (GPM) Mission will take an important step in creating a global precipitation observing system from space based on an international fleet of satellites operated as a constellation. One perspective for understanding the nature of GPM is that it will be a hierarchical system of datastreams beginning with very high caliber combined dual frequency radar/passive microwave (PMW) rain-radiometer retrievals, to high caliber PMW rain-radiometer only retrievals, and then on to blends of the former datastreams with additional lower-caliber PMW-based and IR-based rain retrievals. Within the context of the now emerging global water & energy cycle (GWEC) programs of a number of research agencies throughout the world, GPM serves as a centerpiece space mission for improving our understanding of the Earth's water cycle from a global measurement perspective and on down to regional scales and below. One of the salient problems within our current understanding of the global water and energy cycle is determining whether a change in the rate of the water cycle is accompanying changes in climate, e.g., climate warming. As there are a number of ways in which to define a rate-change of the global water cycle, it is not entirely clear as to what constitutes such a determination. This paper first presents an overview of the GPM Mission and how its overriding scientific objectives for climate, weather, and hydrology flow from the anticipated improvements that are being planned for the constellation-based measuring system. Next, the paper shows how the GPM observations can be used within the framework of the oceanic and continental water budget equations to determine whether a given perturbation in precipitation is indicative of an actual rate change in the water cycle, consistent with required responses in water storage and/or water flux transport processes, or whether it is simply part of the natural variability of a fixed rate cycle.

Smith, Eric A.↗
NASA NTRS2011

GPM Avionics Module Heat Pipes Design and Performance Test Results

The Global Precipitation Measurement (GPM) mission is an international network of satellites that provide the next-generation global observations of rain and snow. The GPM core satellite carries an advanced radar / radiometer system to measure precipitation from space and serve as a reference standard to unify precipitation measurements from a constellation of research and operational satellites. Through improved measurements of precipitation globally, the GPM mission will help to advance our understanding of Earth's water and energy cycle, improve forecasting of extreme events that cause natural hazards and disasters, and extend current capabilities in using accurate and timely information of precipitation to directly benefit society. The avionics module on the core satellite contains a number of electronics boxes which are cooled by a network of aluminum/ammonia heat pipes and a honeycomb radiator which contains thirteen embedded aluminum/ammonia heat pipes. All heat pipes were individually tested by the vendor (Advanced Cooling Technologies, Inc.) prior to delivery. Following delivery to NASA, the flight avionics radiator and the flight spare transport heat pipes were mounted to flight-like test structure and a system level thermal vacuum test was performed. This test, which used simulators in place of all electronics boxes, was done to verify the operation of the thermal control system as a whole. This presentation will discuss the design of the avionics module heat pipes, and then discuss performance tests results for the individual heat pipes prior to delivery and for the system level thermal vacuum test. All heat pipes met their performance requirements. However, it was found that the power was too low in some instances to start all of the smaller radiator spreader heat pipes when they were tested in a reflux configuration (which is the nominal test configuration). Although this lowered the efficiency of the radiator somewhat, it did not impact the operating temperatures of the electronics boxes.

Ottenstein, Laura↗
NASA NTRS2006

Precipitation links (PrecipLinks) - a prototype directory for precipitation information

This poster describes a web directory of research oriented precipitation links. In this era of sophisticated search engines and web agents, it might seem counterproductive to establish such a directory of links. However, entering precipitation into a search engine like google will yield over one million hits. To further exacerbate this situation many of the returned links are dead, duplicates of other links, incomplete, or only marginally related to research precipitation or even the broader precipitation area. Sometimes connecting the linked URL causes the browser to lose context and not be able to get back to the original page. Even using more sophisticated search engines query parameters or agents while reducing the overall return doesn't eliminate all of the other issues listed. As part of the development of the measurement-based Precipitation Processing System (PPS) that will support Tropical Rainfall Measuring Mission (TRMM) version 7 reprocessing and the Global Precipitation Measurement (GPM) mission a precipitation links (PrecipLinks) facility is being developed. PrecipLinks is intended to share locations of other sites that contain information or data pertaining to precipitation research. Potential contributors can log-on to the PrecipLinks website and register their site for inclusion in the directory. The price for inclusion is the requirement to place a link back to PrecipLinks on the webpage that is registered. This ensures that users will be able to easily get back to PrecipLinks regardless of any context issues that browsers might have. Perhaps more importantly users while visiting one site that they know can be referred to a location that has many others sites with which they might not be familiar. PrecipLinks is designed to have a very flat structure. This poster summarizes these categories (information, data, services) and the reasons for their selection. Providers may register multiple pages to which they wish to direct users. However, each page may be attached to only one of these categories. Each page to which they refer users will also have a return link to PrecipLinks. The poster describes the operation of the system both the automated and the human processes. It also provides images for the various steps in the registration and use.

Velanthapillia, Balendran↗
NASA NTRS2011

NASA's Integrated Instrument Simulator Suite for Atmospheric Remote Sensing from Spaceborne Platforms (ISSARS) and Its Role for the ACE and GPM Missions

Forward simulation is an indispensable tool for evaluation of precipitation retrieval algorithms as well as for studying snow/ice microphysics and their radiative properties. The main challenge of the implementation arises due to the size of the problem domain. To overcome this hurdle, assumptions need to be made to simplify compiles cloud microphysics. It is important that these assumptions are applied consistently throughout the simulation process. ISSARS addresses this issue by providing a computationally efficient and modular framework that can integrate currently existing models and is also capable of expanding for future development. ISSARS is designed to accommodate the simulation needs of the Aerosol/Clouds/Ecosystems (ACE) mission and the Global Precipitation Measurement (GPM) mission: radars, microwave radiometers, and optical instruments such as lidars and polarimeter. ISSARS's computation is performed in three stages: input reconditioning (IRM), electromagnetic properties (scattering/emission/absorption) calculation (SEAM), and instrument simulation (ISM). The computation is implemented as a web service while its configuration can be accessed through a web-based interface.

real-aperture instruments↗
NASA NTRS2018

Enriching the Twitter Stream Increasing Data Mining Yield and Quality Using Machine Learning

Social media data streams are important sources of real-time and historical global information for science applications. At the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC), we are exploring the Twitter data stream for its potential in augmenting the validation program of NASA Earth science missions, specifically the Global Precipitation Measurement (GPM) mission. We have implemented a tweet processing infrastructure that outputs classified precipitation tweets. Inputs are "passive" tweets, along with a smaller number of tweets from "active" participants, i.e., those knowingly contributing to our effort. The "active" tweets, presumably of higher quality, enrich the Twitter stream. "Active" sources include data scraped from other social media (e.g., public Facebook posts) and data from existing crowdsourcing programs (e.g., mPING reports). In addition, there is likely relevant precipitation information in images and documents that are the end points of links often included in tweets. Information derived from these "active" sources could then be tweeted into the Twitter stream, thus enriching its quality. The objective of our current work is to mine these tweet­ linked images and documents, using neural networks, to increase the information content and quality related to precipitation. For images, we classified them as either precipitation-related or not. For training and validation, we used images obtained via the Google custom search API. We created two models: (1) by training a simple Convolutional Neural Network and (2) by using transfer learning principles to adapt a pre-trained object recognition model. For documents, both those linked to tweets and the tweet contents, we trained Hierarchical Attention Networks to determine precipitation occurrence, type, and intensity. For training and validation, we used a keyword-filtered tweet data set labelled with ground truth data from Dark Sky (an API to retrieve weather-related labels) and the National Severe Storms Laboratory's Multi­ Radar/Multi-Sensor (MRMS) system. Our results demonstrated the efficacy of our machine learning approaches for enriching the Twitter stream, to derive information potentially useful for validation of earth science satellite data.

Albayrak, Arif↗
NASA NTRS2002

Relationship Between GPM Mission and NASA's Global Water Cycle Research Program

During the coming decade, the internationally organized Global Precipitation Measurement (GPM) Mission will take an important step in creating a global precipitation observing system from space based on an international fleet of satellites operated as a constellation of opportunity. One perspective for understanding the nature of GPM is that it will be a hierarchical system of datastreams beginning with very high caliber combined dual frequency radar/passive microwave (PMW) rain-radiometer retrievals, to high caliber PMW rain- radiometer only retrievals, and then on to blends of the former datastreams with additional lower-caliber PMW-based and IR-based rain retrievals. Within the context of the now emerging global water & energy cycle (GWEC) programs of a number of research agencies throughout the world, GPM serves as a centerpiece space mission for improving our understanding of the Earth's water cycle from a global measurement perspective and on down to regional scales and below. One of the salient problems within our current understanding of the global water and energy cycle is determining whether a change in the rate of the water cycle is accompanying changes in climate, e.g., climate warming. As there are a number of ways in which to define a rate- change of the global water cycle, it is not entirely clear as to what constitutes such a determination. This paper first presents an overview of the GPM Mission and how its overriding scientific objectives for climate, weather, and hydrology flow from the anticipated improvements that are being planned for the constellation-based measuring system. Next, the paper shows how the GPM observations can be used within the framework of the oceanic and continental water budget equations to determine whether a given perturbation in precipitation is indicative of an actual rate change in the water cycle, consistent with required responses in water storage and/or water flux transport processes, or whether it is simply part of the natural variability of a fixed rate cycle.

Smith, Eric A.↗
NASA NTRS2003

NASA's Scientific Agenda for GPM Mission

The objectives of the Global Precipitation Measurement (GPM) Mission are to: 1) Understand the horizontal and vertical structure of rainfall, its macro- and micro-physical nature, and its associated latent heating, 2) Train and calibrate retrieval algorithms for constellation radiometers, 3) Provide sufficient global sampling to significantly reduce uncertainties in short-term rainfall accumulations, and 4) Extend scientific and societal applications. The GPM mission design consists of a core satellite, constellation satellties, precipitation validation sites for error characterization, and a precipitation processing center. This viewgraph presentation profiles the science goals of the mission, and its capability for producing climate data and weather predictions.

Smith, Eric A.↗