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At least 37 records · Page 2

Eastern Pacific Cloud Aerosol Precipitation Experiment (EPCAPE) Science Plan

Coastal cities provide the opportunity to characterize marine clouds and the substantial effects of manmade particles on cloud properties and processes. La Jolla lies to the north of San Diego, California, but it is often about a day directly downwind of the major pollution sources located in the ports of Los Angeles and Long Beach. The large dynamic range of aerosol particle concentrations combined with the multi-hour to multi-day persistence of stratocumulus cloud layers makes the site ideal for investigating the seasonal changes in cloud and aerosol properties as well as the quantitative relationships between cloud and aerosol properties.

54 ENVIRONMENTAL SCIENCES↗

The Global Precipitation Measurement (GPM) Mission: Overview and U.S. Science Status

The Global Precipitation Measurement (GPM) Mission, an international satellite mission to unify and advance space-based precipitation measurements around the globe, is a science mission with integrated application goals. The mission is designed to (1) advance the knowledge of the global water cycle and freshwater availability, and (2) improve weather, climate, and hydrological prediction capabilities through more accurate and frequent measurements of global precipitation. The cornerstone of GPM is the deployment of a Core Spacecraft in a unique 65 deg-inclined orbit to serve as a physics observatory and a calibration reference to improve the accuracy of precipitation measurements by a heterogeneous constellation of dedicated and operational passive microwave sensors. The Core Spacecraft will carry a dual-frequency (Ku-Ka band) radar and a multi-channel microwave radiometer with high-frequency capabilities to provide measurements of 3-D precipitation structures and microphysical properties, which are key to achieving a better understanding of precipitation processes and improved retrieval algorithms for passive microwave radiometers. The GPM constellation is envisioned to comprise 5 or more conical-scanning microwave radiometers provided by partners, augmented by cross-track microwave sounders on operational satellites such as the National Polar-orbiting Operational Environmental Satellite System (NPOESS) Preparatory Project (NPP), POES, NPOESS, and MetOp satellites for improved sampling over land. The GPM Mission is currently a partnership between NASA and the Japan Aerospace Exploration Agency (JAXA), with opportunities for additional international partners in constellation satellites and ground validation. An overview of the GPM mission concept and science activities in the United States will be presented.

Hou, Arthur Y.↗

The Global Precipitation Measurement (GPM) Mission: Overview and U.S. Science Status

The Global Precipitation Measurement (GPM) Mission, an international satellite mission to unify and advance space-based precipitation measurements around the globe, is a science mission with integrated application goals. The mission is designed to (1) advance the knowledge of the global water cycle and freshwater availability, and (2) improve weather, climate, and hydrological prediction capabilities through more accurate and frequent measurements of global precipitation. The cornerstone of GPM is the deployment of a Core Spacecraft in a unique 65 deg-inclined orbit to serve as a physics observatory and a calibration reference to improve the accuracy of precipitation measurements by a heterogeneous constellation of dedicated and operational passive microwave sensors. The Core Spacecraft will carry a dual-frequency (Ku-Ka band) radar and a multi-channel microwave radiometer with high-frequency capabilities to provide measurements of 3-D precipitation structures and microphysical properties, which are key to achieving a better understanding of precipitation processes and improved retrieval algorithms for passive microwave radiometers. The GPM constellation is envisioned to comprise 5 or more conical-scanning microwave radiometers provided by partners, augmented by cross-track microwave sounders on operational satellites such as the National Polar-orbiting Operational Environmental Satellite System (NPOESS) Preparatory Project (NPP), POES, NPOESS, and MetOp satellites for improved sampling over land. The GPM Mission is currently a partnership between NASA and the Japan Aerospace Exploration Agency (JAXA), with opportunities for additional international partners in constellation satellites and ground validation. An overview of the GPM mission concept and science activities in the United States will be presented.

Hou, Arthur↗

NASA Observations and Modeling During ICE-POP

Recap: NASA-Specific Objectives for ICE-POP: Provide real-time observational and NWP data in support of ICE-POP, participate in significant international science effort; GPM (Global Precipitation Measurement) Ground Validation and NASA Weather Program -Direct/physical validation of active/passive satellite-based snowfall retrieval algorithms over coastline and mountains; melting layer interaction with terrain -Physics of snow, coupling to snow water equivalent rate and satellite remote sensor retrieval algorithm assumptions - -Size distributions, types/habit, water equivalent, profiles -NU-WRF (NASA-Unified Weather Research and Forecasting) Model plus Observational analyses: Movement toward “level IV products” leverage intensive and multi-faceted NWP (Numerical Weather Prediction) component -Model precipitation processes (liquid, mixed phase and frozen); Build model testing database for further active/passive remote sensing algorithm development (e.g., satellite data simulators) -"Integrated" validation of products in operational context.

Precipitation Science↗

NGEE Arctic Rainfall Simulator Validation Data from Los Alamos National Laboratory, New Mexico, Summer 2022

Experiments evaluating the uniformity and intensity of rainfall produced by the NGEE Arctic Rainfall Simulator (NARS) were conducted at Los Alamos National Laboratory, New Mexico, over summer 2022. Petri dishes were placed in a grid within the NARS plot. Simulated rainfall was collected in each petri dish and the intensity and uniformity of the simulator was subsequently calculated. This data package contains two .csv files, one that summarizes the rainfall intensity and uniformity for each experiment, the other that contains individual petri dish water volume and intensity for each plot location and experiment. The Python scripts to control NARS are also included. The NGEE Arctic Rainfall Simulator (NARS) is a variable intensity rainfall simulator (RFS) with a frame design based on the Humphry et al. (2002) RFS and a water delivery system based on the Walnut Gulch (Paige et al., 2004) RFS. The NARS uses an aluminum frame that is fully deconstructable for transportation to field locations and a water system that enables variable rain intensity. Rain intensity control and data collection are automated using a Raspberry Pi microcomputer. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a 15-year research effort (2012-2027) to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗

Cloud and Precipitation Experiment at Kennaook (Cape-K) Science Plan

The Cloud and Precipitation Experiment at Kennaook (CAPE-K) will augment ongoing measurements at the Kennaook/Cape Grim Baseline Air Pollution Station (KCG) with components of the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility’s second Mobile Facility (AMF2) in a deployment that is scheduled to extend from April 2024 through September 2025. Located at Kennaook/Cape Grim on the northwestern tip of Tasmania (40.68° S, 144.69° E), KCG has produced the longest and most consequential record of Southern Hemisphere aerosol and gas-phase chemistry; however, extensive cloud and precipitation measurements have not been collected at this site. CAPE-K is motivated by the fact that model uncertainty in the surface radiation budget due to clouds and precipitation remain significant in this region, where strong latitudinal gradients in cloud feedbacks are found and there are strong and well-documented seasonal variations in marine aerosol properties. Satellite data suggest that cloud properties co-vary with the seasonal cycle in aerosol in the Southern Ocean (SO), yet detailed vertically resolved measurements of cloud and precipitation in the marine boundary layer are sparse. CAPE-K will provide the first such seasonal cycle of measurements in this important latitude band. CAPE-K will provide detailed cloud and precipitation observations over two winter seasons and align with complementary high-value-add activities planned by Australian colleagues, including a voyage of the Research Vessel Investigator (RVI) in May 2025 that will conduct measurements just offshore of KCG.

54 ENVIRONMENTAL SCIENCES↗

Science Formulation of Global Precipitation Mission (GPM)

In late 2001, the Global Precipitation Measurement (GPM) mission was approved as a new start by the National Aeronautics and Space Administration (NASA). The new mission, which is now in its formulation phase, is motivated by a number of scientific questions that are posed over a range of space and time scales that generally fall within the discipline of the global water and energy cycle (GWEC), although not restricted to that branch of research. Recognizing that satellite rainfall datasets are now a foremost tool for understanding global climate variability out to decadal scales and beyond, for improving weather forecasting, and for producing better predictions of hydrometeorological processes including short-term hazardous flooding and seasonal fresh water resources assessment, a comprehensive and internationally sanctioned global measuring strategy has led to the GPM mission. The GPM mission plans to expand the scope of rainfall measurement through use of a multi-member satellite constellation that will be contributed by a number of world nations. This talk overviews the GPM scientific research program that has been fostered within NASA, then focuses on scientific progress that is being made in various areas in the course of the mission formulation phase that are of interest to the Natural Hazards scientific community. This latter part of the talk addresses research issues that have become central to the GPM science implementation plan concerning the rate of the global water cycling, cloud macrophysical-microphysical processes of flood-producing storms, and the general improvement in measuring precipitation at the fundamental microphysical level.

Smith, Eric A.↗

Chapter 19: Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (GPM) mission (IMERG)

The Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (GPM) mission (IMERG) is a U.S. GPM Science Team precipitation product. IMERG uses intercalibrated estimates from the international constellation of precipitation-relevant satellites and other data, including monthly surface precipitation gauge analyses, to compute half hour, 0.1° x 0.1° gridded datasets over 60°N-S (and partially outside of that latitude band) in three “Runs”—Early (4 h after obs time), Late (14 h after obs time), and Final (3.5 months after obs time). The concepts behind IMERG are briefly reviewed, together with major shifts related to changes in versions from the at-launch Version 03 to Version 05, and an outline of Version 06, which was released in late 2019.

George John Huffman↗

Duke Forest FACE (FACTS-I): Meteorological and Soil Data

This dataset, collected from Duke Forest Free Air CO2 Enrichment (FACE) – Forest-Atmosphere Carbon Transfer and Storage (FACTS-I) experiment, includes variables describing the meteorological conditions above canopy, within canopy, and soil depending on the variable. The Duke FACE experiment was located in a loblolly pine (Pinus taeda L.) plantation established in 1983. Naturally regenerated broadleaved species including sweetgum (Liquidambar styraciflua L.) and tulip poplar (Liriodendron tulipifera L.), mostly in the overstory, and winged elm (Ulmus alata Michx.) and red maple (Acer rubrum L.) were common in the understory. The FACE experiment commenced with two plots (plots 7-8) in 1994 (Oren et al. 2001), with six additional plots (plots 1-6) coming online on 27 August 1996. The CO2 enrichment was terminated on 31 October 2010 and post-enrichment data collection continued through 2012. Complete fertilization was applied annually to half of plots 7-8 from 1998 to 2004. The nutrient addition experiment expanded to half of plot 1-6 with a common protocol of N-fertilization in 2005. N-fertilization continued until 2012. The data range varied by sensor availability. A summary of information about variable name and data range can be found in the ‘FileDescription_[variable_name].txt’ files.

54 ENVIRONMENTAL SCIENCES↗

Environmental monitoring data from the 2022-2023 field experiment at Game Ridge, Missoula County, Montana, USA

This dataset contains sensor data from programmed loggers as well as handheld moisture probes, including weather data, air and soil temperature, and soil volumetric water content. Data files and data dictionary(ies) are uploaded as .csv files. The Users Guide is a .pdf file. Location data can be found in the Google Earth file GameRidge_SitePlotCoordinates.kmz.kml included here. These datasets were collected for Plant Carbohydrate Depletion, Mycorrhizal Networks, and Vulnerability to Drought: An Experimental Test in the Field. This experiment examined the interdependency between plant hydraulics and carbohydrate availability and sought to develop ways to incorporate interactions with below ground symbiotic organisms to better model and quantify forest response to drought. This environmental data was collected to provide context for the fungal community data and Pinus ponderosa physiological data.

54 ENVIRONMENTAL SCIENCES↗

Precipitation from Space: Advancing Earth System Science

Of the three primary sources of spatially contiguous precipitation observations (surface networks, ground-based radar, and satellite-based radar/radiometers), only the last is a viable source over ocean and much of the Earth's land. As recently as 15 years ago, users needing quantitative detail of precipitation on anything under a monthly time scale relied upon products derived from geostationary satellite thermal infrared (IR) indices. The Special Sensor Microwave Imager (SSMI) passive microwave (PMW) imagers originated in 1987 and continue today with the SSMI sounder (SSMIS) sensor. The fortunate longevity of the joint National Aeronautics and Space Administration (NASA) and Japan Aerospace Exploration Agency (JAXA) Tropical Rainfall Measuring Mission (TRMM) is providing the environmental science community a nearly unbroken data record (as of April 2012, over 14 years) of tropical and sub-tropical precipitation processes. TRMM was originally conceived in the mid-1980s as a climate mission with relatively modest goals, including monthly averaged precipitation. TRMM data were quickly exploited for model data assimilation and, beginning in 1999 with the availability of near real time data, for tropical cyclone warnings. To overcome the intermittently spaced revisit from these and other low Earth-orbiting satellites, many methods to merge PMW-based precipitation data and geostationary satellite observations have been developed, such as the TRMM Multisatellite Precipitation Product and the Climate Prediction Center (CPC) morphing method (CMORPH. The purpose of this article is not to provide a survey or assessment of these and other satellite-based precipitation datasets, which are well summarized in several recent articles. Rather, the intent is to demonstrate how the availability and continuity of satellite-based precipitation data records is transforming the ways that scientific and societal issues related to precipitation are addressed, in ways that would not be otherwise possible. These developments have taken place in parallel with the growth of an increasingly interconnected scientific environment. Scientists from different disciplines can easily interact with each other via information and materials they encounter online, and collaborate remotely without ever meeting each other in person. Likewise, these precipitation datasets are quickly and easily available via various data portals and are widely used. Within the framework of the NASA/JAXA Global Precipitation Measurement (GPM mission, these applications will become increasingly interconnected. We emphasize that precipitation observations by themselves provide an incomplete picture of the state of the atmosphere. For example, it is unlikely that a richer understanding of the global water cycle will be possible by standalone missions and algorithms, but must also involve some component of data, where model analyses of the physical state are constrained alongside multiple observations (e.g., precipitation, evaporation, radiation). The next section provides examples extracted from the many applications that use various high-resolution precipitation products. The final section summarizes the future system for global precipitation processing.

Kucera, Paul A.↗

precipipmp.a1

Precipitation Image Packaging data

54 ENVIRONMENTAL SCIENCES↗

rain (a1)

A rain gauge (RAIN) gathers and measures the amount of liquid precipitation over a set period of time. ARM uses many types of rain gauges at its climate research facilities. The tipping bucket rain gauge consists of a funnel that collects and directs precipitation into a seesaw container that tips after a pre-set amount of liquid enters. When the seesaw tips, it sends an electrical signal that is counted by a recording device. The weighing precipitation gauge consists of a storage container, which is weighted to record the mass of precipitation. Some models measure the mass of precipitation using a pen on a rotating drum or by using a vibrating wire attached to a data logger. The advantage of this type of rain gauge is that it can measure all forms of precipitation. Disdrometers are used with rain gauges to add details about the precipitation being gathered. They are capable of measuring the velocity and size distribution of precipitation, measurements important for studying the evolution of water droplets.

54 ENVIRONMENTAL SCIENCES↗

rain (b1)

A rain gauge (RAIN) gathers and measures the amount of liquid precipitation over a set period of time. ARM uses many types of rain gauges at its climate research facilities. The tipping bucket rain gauge consists of a funnel that collects and directs precipitation into a seesaw container that tips after a pre-set amount of liquid enters. When the seesaw tips, it sends an electrical signal that is counted by a recording device. The weighing precipitation gauge consists of a storage container, which is weighted to record the mass of precipitation. Some models measure the mass of precipitation using a pen on a rotating drum or by using a vibrating wire attached to a data logger. The advantage of this type of rain gauge is that it can measure all forms of precipitation. Disdrometers are used with rain gauges to add details about the precipitation being gathered. They are capable of measuring the velocity and size distribution of precipitation, measurements important for studying the evolution of water droplets.

54 ENVIRONMENTAL SCIENCES↗

Exploring variability in seasonal average and extreme precipitation using unsupervised machine learning.

Focal Area(s): We will use unsupervised machine learning methods to identify and quantify the influence of large scale natural modes of climate variability to gain insight into the observed and simulated seasonal average and extreme precipitation changes. Science Challenge: A recent paper, led by co-PI Mark Risser, finds that although much of the variability in seasonal average and extreme precipitation over CONUS is unforced, the effect of large-scale modes of circulation variability (such as ENSO, AMO, PNA, etc.) can be detected and attributed. However, it is unclear whether or not unsupervised learning methods can (a) replicate this finding or (b) yield insight into possible nonlinear behavior that was not captured in the initial statistical analysis. Further work would entail extending this framework to other global land areas.

54 ENVIRONMENTAL SCIENCES↗

CROCUS Tipping Bucket Rain Gauge Data from Argonne Deployable Mast Deployed at NEIU Carruthers Center for Inner City Studies (CCICS)

The Tipping Bucket Rain Gauge (TBRG) dataset contains data from a non-heated Met One 12-inch tipping bucket rain gauge that was mounted on the Argonne Deployable Mast (ADM). The ADM is a rapid deployable meteorological trailer that can be outfitted with instrumentation to measure urban heat island effects, urban flooding or urban flux measurements. During the urban flooding field campaign, the ADM was outfitted with multiple precipitation measurement systems, including the TBRG. This dataset contains one minute measurements for precipitation accumulation during the ADM's deployment at the Northeastern Illinois University (NEIU) Carruthers Center for Inner City Studies (CCICS) campus. These data are helpful for identifying periods of precipitation, leading to potential flooding. TBRGs can be used to validate optical rain gauge data and disdrometer data collected during the CROCUS urban flooding campaign. Data were collected at the CCICS building parking lot, located in the south side of Chicago, IL. The data is presented as daily NetCDF (.nc) files, each containing approximately 24 hours of observations. Files follow the naming convention of: the project (CROCUS), location (ADM-ccics), instrument name (tbrg), data level (raw, a1), and date (year, month, day). The NetCDF format can be accessed using common scientific software such as Python using xarray, netCDF4 or act-doe.

1-minute Precipitation Accumulation↗