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NASA NTRS2013

Anti-Icing Formulas Prevent Train Delays

In the winter of 2009, Washington, DC, workers faced the prospect of a difficult commute due to record-setting snowfalls. But thousands of the city's Metrorail riders found the public transportation system fully functional, thanks in part to a NASA technology invented years before. Just like trains, an airplane must be snow- and ice-free to ensure safe travel. Traditionally, fluids containing a compound called ethylene glycol have been used to inhibit ice on planes. In 1992, however, the US Air Force banned its purchase of this ingredient due to toxicity concerns. According to the Centers for Disease Control, exposure to large amounts of ethylene glycol through air or water can damage the kidneys, nervous system, lungs, and heart. Urine samples from airport deicing workers have contained traces of the substance. At the time of the Air Force s ban, Robert Lockyer was working at NASA s Ames Research Center in the Advanced Composites Model Development Branch, where he says "we decided to pick up the gauntlet and began researching existing fluid compositions and the processes used in deicing aircraft." Along with Lockyer, in 1997 Ames researchers Leonard Haslim and John Zuk devised a nontoxic, biodegradable, and cost effective substitute for ethylene glycol. When applied to a dry surface before a snow or ice event, the solution prevented ice from forming a bond with the surface. This made it easy to wipe away any accumulation.

Source record↗

NASA NTRS2013

Using Air Temperature to Quantitatively Predict the MODIS Fractional Snow Cover Retrieval Errors over the Continental US (CONUS)

In the middle to high latitude and alpine regions, the seasonal snow pack can dominate the surface energy and water budgets due to its high albedo, low thermal conductivity, high emissivity, considerable spatial and temporal variability, and ability to store and then later release a winters cumulative snowfall (Cohen, 1994; Hall, 1998). With this in mind, the snow drought across the U.S. has raised questions about impacts on water supply, ski resorts and agriculture. Knowledge of various snow pack properties is crucial for short-term weather forecasts, climate change prediction, and hydrologic forecasting for producing reliable daily to seasonal forecasts. One potential source of this information is the multi-institution North American Land Data Assimilation System (NLDAS) project (Mitchell et al., 2004). Real-time NLDAS products are used for drought monitoring to support the National Integrated Drought Information System (NIDIS) and as initial conditions for a future NCEP drought forecast system. Additionally, efforts are currently underway to assimilate remotely-sensed estimates of land-surface states such as snowpack information into NLDAS. It is believed that this assimilation will not only produce improved snowpack states that better represent snow evolving conditions, but will directly improve the monitoring of drought.

MODIS↗

NASA NTRS2012

Snow Dunes: A Controlling Factor of Melt Pond Distribution on Arctic Sea Ice

The location of snow dunes over the course of the ice-growth season 2007/08 was mapped on level landfast first-year sea ice near Barrow, Alaska. Landfast ice formed in mid-December and exhibited essentially homogeneous snow depths of 4-6 cm in mid-January; by early February distinct snow dunes were observed. Despite additional snowfall and wind redistribution throughout the season, the location of the dunes was fixed by March, and these locations were highly correlated with the distribution of meltwater ponds at the beginning of June. Our observations, including ground-based light detection and ranging system (lidar) measurements, show that melt ponds initially form in the interstices between snow dunes, and that the outline of the melt ponds is controlled by snow depth contours. The resulting preferential surface ablation of ponded ice creates the surface topography that later determines the melt pond evolution.

arctic↗

NASA NTRS2014

Signatures of Hydrometeor Species from Airborne Passive Microwave Data for Frequencies 10-183 GHz

There are 2 basic precipitation retrieval methods using passive microwave measurements: (1) Emission-based: Based on the tendency of liquid precipitation to cause an increase in brightness temperature (BT) primarily at frequencies below 22 GHz over a radiometrically cold background, often an ocean background (e.g., Spencer et al. 1989; Adler et al. 1991; McGaughey et al. 1996); and (2) Scattering-based: Based on the tendency of precipitation-sized ice to scatter upwelling radiation, thereby reducing the measured BT over a relatively warmer (usually land) background at frequencies generally 37 GHz (e.g., Spencer et al. 1989; Smith et al. 1992; Ferraro and Marks 1995). Passive microwave measurements have also been used to detect intense convection (e.g., Spencer and Santek 1985) and for the detection of hail (e.g., Cecil 2009; Cecil and Blankenship 2012; Ferraro et al. 2014). The Global Precipitation Measurement (GPM) mission expands upon the successful Tropical Rainfall Measurement Mission program to provide global rainfall and snowfall observations every 3 hours (Hou et al. 2014). One of the instruments on board the GPM Core Observatory is the GPM Microwave Imager (GMI) which is a conically-scanning microwave radiometer with 13 channels ranging from 10-183 GHz. Goal of this study: Determine the signatures of various hydrometeor species in terms of BTs measured at frequencies used by GMI by using data collected on 3 case days (all having intense/severe convection) during the Mid-latitude Continental Convective Clouds Experiment conducted over Oklahoma in 2011.

Cecil, Daniel J.↗

NASA NTRS2013

Application and Evaluation of a Snowmelt Runoff Model in the Tamor River Basin, Eastern Himalaya Using a Markov Chain Monte Carlo (MCMC) Data Assimilation Approach

Previous studies have drawn attention to substantial hydrological changes taking place in mountainous watersheds where hydrology is dominated by cryospheric processes. Modelling is an important tool for understanding these changes but is particularly challenging in mountainous terrain owing to scarcity of ground observations and uncertainty of model parameters across space and time. This study utilizes a Markov Chain Monte Carlo data assimilation approach to examine and evaluate the performance of a conceptual, degree-day snowmelt runoff model applied in the Tamor River basin in the eastern Nepalese Himalaya. The snowmelt runoff model is calibrated using daily streamflow from 2002 to 2006 with fairly high accuracy (average Nash-Sutcliffe metric approx. 0.84, annual volume bias <3%). The Markov Chain Monte Carlo approach constrains the parameters to which the model is most sensitive (e.g. lapse rate and recession coefficient) and maximizes model fit and performance. Model simulated streamflow using an interpolated precipitation data set decreases the fractional contribution from rainfall compared with simulations using observed station precipitation. The average snowmelt contribution to total runoff in the Tamor River basin for the 2002-2006 period is estimated to be 29.7+/-2.9% (which includes 4.2+/-0.9% from snowfall that promptly melts), whereas 70.3+/-2.6% is attributed to contributions from rainfall. On average, the elevation zone in the 4000-5500m range contributes the most to basin runoff, averaging 56.9+/-3.6% of all snowmelt input and 28.9+/-1.1% of all rainfall input to runoff. Model simulated streamflow using an interpolated precipitation data set decreases the fractional contribution from rainfall versus snowmelt compared with simulations using observed station precipitation. Model experiments indicate that the hydrograph itself does not constrain estimates of snowmelt versus rainfall contributions to total outflow but that this derives from the degree-day melting model. Lastly, we demonstrate that the data assimilation approach is useful for quantifying and reducing uncertainty related to model parameters and thus provides uncertainty bounds on snowmelt and rainfall contributions in such mountainous watersheds.

runoff↗

NASA NTRS2014

Surge Pressure Mitigation in the Global Precipitation Measurement Mission Core Propulsion System

The Global Precipitation Measurement (GPM) mission is an international partnership between NASA and JAXA whose Core spacecraft performs cutting-edge measurements of rainfall and snowfall worldwide and unifies data gathered by a network of precipitation measurement satellites. The Core spacecraft's propulsion system is a blowdown monopropellant system with an initial hydrazine load of 545 kg in a single composite overwrapped propellant tank. At launch, the propulsion system contained propellant in the tank and manifold tubes upstream of the latch valves, with low-pressure helium gas in the manifold tubes downstream of the latch valves. The system had a relatively high beginning-of- life pressure and long downstream manifold lines; these factors created conditions that were conducive to high surge pressures. This paper discusses the GPM project's approach to surge mitigation in the propulsion system design. The paper describes the surge testing program and results, with discussions of specific difficulties encountered. Based on the results of surge testing and pressure drop analyses, a unique configuration of cavitating venturis was chosen to mitigate surge while minimizing pressure losses during thruster maneuvers. This paper concludes with a discussion of overall lessons learned with surge pressure testing for NASA Goddard spacecraft programs.

Pressure↗

NASA NTRS2015

Global Precipitation Measurement Cold Season Precipitation Experiment (GCPEx): For Measurement Sake Let it Snow

As a component of the Earth's hydrologic cycle, and especially at higher latitudes,falling snow creates snow pack accumulation that in turn provides a large proportion of the fresh water resources required by many communities throughout the world. To assess the relationships between remotely sensed snow measurements with in situ measurements, a winter field project, termed the Global Precipitation Measurement (GPM) mission Cold Season Precipitation Experiment (GCPEx), was carried out in the winter of 2011-2012 in Ontario, Canada. Its goal was to provide information on the precipitation microphysics and processes associated with cold season precipitation to support GPM snowfall retrieval algorithms that make use of a dual-frequency precipitation radar and a passive microwave imager on board the GPM core satellite,and radiometers on constellation member satellites. Multi-parameter methods are required to be able to relate changes in the microphysical character of the snow to measureable parameters from which precipitation detection and estimation can be based. The data collection strategy was coordinated, stacked, high-altitude and in-situ cloud aircraft missions with three research aircraft sampling within a broader surface network of five ground sites taking in-situ and volumetric observations. During the field campaign 25 events were identified and classified according to their varied precipitation type, synoptic context, and precipitation amount. Herein, the GCPEx fieldcampaign is described and three illustrative cases detailed.

GCPEx↗

NASA NTRS2015

Early Mars Climate Modeling and the Faint Young Sun Paradox.

Today Mars is a cold, dry, desert planet. Liquid water is not stable on its surface. There are no lakes, seas, or oceans, and precipitation falls as snowfall. Yet early in its history during the Noachian epoch, there is geological and mineralogical evidence that liquid water from rainfall flowed on its surface creating drainage systems, lakes, and - possibly - seas and oceans. More recent observations by Curiosity in Gale crater hint that such conditions may have persited into the Hesperian. The implication is that early Mars had a wamer climate than it does today as a result of a thicker atmosphere with a more powerful greenhouse effect capable of producing an active hydrological cycle with rainfall, runoff, and evaporation. Since Mariner 9 began accumulating such evidence, researchers have been trying to understand what kind of a climate system could have created greenhouse conditions favorable for liquid water. Unfortunately, the problem is not yet solved.

greenhouse gases↗

NASA NTRS2016

Impact of Source Region on the delta18 O signal in snow: A case study from Mount Wrangell Alaska

The stable isotopic composition of water in ice cores is an important source of information on past climate variability. At its simplest level, the underlying assumption is that there is an empirical relationship between the normalized difference in the concentration for these stable isotopes and a specified local temperature at the ice core site. There are, however, nonlocal processes, such as a change in source region or a change in the atmospheric pathway, which can impact the stable isotope signal, thereby complicating its use as a proxy for temperature. In this paper, the importance of these nonlocal processes are investigated through the analysis of the synoptic-scale circulation during a snowfall event at the summit of Mount Wrangell (62 deg N, 144 deg W; 4300 m MSL) in south-central Alaska. During this event there was, over a 1-day period in which the local temperature was approximately constant, a change in delta18 O that exceeded half that normally seen to occur in the region between summer and winter. As shall be shown, this arose from a change in the source region, from the subtropical eastern Pacific to northeastern Asia, for the snow that fell on Mount Wrangell during the event.

Snow↗

NASA NTRS2016

Performance of the Falling Snow Retrieval Algorithms for the Global Precipitation Measurement (GPM) Mission

Retrievals of falling snow from space represent an important data set for understanding the Earth's atmospheric, hydrological, and energy cycles, especially during climate change. Estimates of falling snow must be captured to obtain the true global precipitation water cycle, snowfall accumulations are required for hydrological studies, and without knowledge of the frozen particles in clouds one cannot adequately understand the energy and radiation budgets. While satellite-based remote sensing provides global coverage of falling snow events, the science is relatively new and retrievals are still undergoing development with challenges remaining). This work reports on the development and testing of retrieval algorithms for the Global Precipitation Measurement (GPM) mission Core Satellite, launched February 2014.

Microwave satellite↗

NASA NTRS2016

The Abandoned Ice Sheet Base at Camp Century, Greenland, in a Warming Climate

In 1959 the U.S. Army Corps of Engineers built Camp Century beneath the surface of the northwestern Greenland Ice Sheet. There they studied the feasibility of deploying ballistic missiles within the ice sheet. The base and its wastes were abandoned with minimal decommissioning in 1967, under the assumption they would be preserved for eternity by perpetually accumulating snowfall. Here we show that a transition in ice sheet surface mass balance at Camp Century from net accumulation to net ablation is plausible within the next 75years, under a business-as-usual anthropogenic emissions scenario (Representative Concentration Pathway 8.5). Net ablation would guarantee the eventual remobilization of physical, chemical, biological, and radiological wastes abandoned at the site. While Camp Century and four other contemporaneous ice sheet bases were legally established under a Danish-U.S. treaty, the potential remobilization of their abandoned wastes, previously regarded as sequestered, represents an entirely new pathway of political dispute resulting from climate change.

ice sheet↗

NASA NTRS2017

Short-Term Forecasts Using NU-WRF for the Winter Olympics 2018

The NASA Unified-Weather Research and Forecasting model (NU-WRF) will be included for testing and evaluation in the forecast demonstration project (FDP) of the International Collaborative Experiment −PyeongChang 2018 Olympic and Paralympic (ICE-POP) Winter Games. An international array of radar and supporting ground based observations together with various forecast and now-cast models will be operational during ICE-POP. In conjunction with personnel from NASA's Goddard Space Flight Center, the NASA Short-term Prediction Research and Transition (SPoRT) Center is developing benchmark simulations for a real-time NU-WRF configuration to run during the FDP. ICE-POP observational datasets will be used to validate model simulations and investigate improved model physics and performance for prediction of snow events during the research phase (RDP) of the project The NU-WRF model simulations will also support NASA Global Precipitation Measurement (GPM) Mission ground-validation physical and direct validation activities in relation to verifying, testing and improving satellite-based snowfall retrieval algorithms over complex terrain.

forecasting↗

NASA NTRS2017

An Application Programming Interface for Synthetic Snowflake Particle Structure and Scattering Data

The work by Kuo and colleagues on growing synthetic snowflakes and calculating their single-scattering properties has demonstrated great potential to improve the retrievals of snowfall. To grant colleagues flexible and targeted access to their large collection of sizes and shapes at fifteen (15) microwave frequencies, we have developed a web-based Application Programming Interface (API) integrated with NASA Goddard's Precipitation Processing System (PPS) Group. It is our hope that the API will enable convenient programmatic utilization of the database. To help users better understand the API's capabilities, we have developed an interactive web interface called the OpenSSP API Query Builder, which implements an intuitive system of mechanisms for selecting shapes, sizes, and frequencies to generate queries, with which the API can then extract and return data from the database. The Query Builder also allows for the specification of normalized particle size distributions by setting pertinent parameters, with which the API can also return mean geometric and scattering properties for each size bin. Additionally, the Query Builder interface enables downloading of raw scattering and particle structure data packages. This presentation will describe some of the challenges and successes associated with developing such an API. Examples of its usage will be shown both through downloading output and pulling it into a spreadsheet, as well as querying the API programmatically and working with the output in code.

reflectivity properties of snowflakes↗

NASA NTRS2013

Detection Thresholds of Falling Snow From Satellite-Borne Active and Passive Sensors

There is an increased interest in detecting and estimating the amount of falling snow reaching the Earths surface in order to fully capture the global atmospheric water cycle. An initial step toward global spaceborne falling snow algorithms for current and future missions includes determining the thresholds of detection for various active and passive sensor channel configurations and falling snow events over land surfaces and lakes. In this paper, cloud resolving model simulations of lake effect and synoptic snow events were used to determine the minimum amount of snow (threshold) that could be detected by the following instruments: the W-band radar of CloudSat, Global Precipitation Measurement (GPM) Dual-Frequency Precipitation Radar (DPR)Ku- and Ka-bands, and the GPM Microwave Imager. Eleven different nonspherical snowflake shapes were used in the analysis. Notable results include the following: 1) The W-band radar has detection thresholds more than an order of magnitude lower than the future GPM radars; 2) the cloud structure macrophysics influences the thresholds of detection for passive channels (e.g., snow events with larger ice water paths and thicker clouds are easier to detect); 3) the snowflake microphysics (mainly shape and density)plays a large role in the detection threshold for active and passive instruments; 4) with reasonable assumptions, the passive 166-GHz channel has detection threshold values comparable to those of the GPM DPR Ku- and Ka-band radars with approximately 0.05 g *m(exp -3) detected at the surface, or an approximately 0.5-1.0-mm * h(exp -1) melted snow rate. This paper provides information on the light snowfall events missed by the sensors and not captured in global estimates.

remote sensing↗

NASA NTRS2018

Impact of GPM Rainrate Data Assimilation on Simulation of Hurricane Harvey (2017)

Built upon Tropical Rainfall Measuring Mission (TRMM) legacy for next-generation global observation of rain and snow. The GPM was launched in February 2014 with Dual-frequency Precipitation Radar (DPR) and GPM Microwave Imager (GMI) onboard. The GPM has a broad global coverage approximately 70deg S -70deg N with a swath of 245/125-km for the Ka (35.5 GHz)/Ku (13.6 GHz) band radar, and 850-km for the 13-channel GMI. GPM also features better retrievals for heavy, moderate, and light rain and snowfall To develop methodology to assimilate GPM surface precipitation data with Grid-point Statistical Interpolation (GSI) data assimilation system and WRF ARW model To investigate the potential and the value of utilizing GPM observation into NWP for operational environment The GPM rain rate data has been successfully assimilated using the GSI rain data assimilation package. Impacts of rain rate data have been found in temperature and moisture fields of initial conditions. 2.Assimilation of either GPM IMERG or GPROF rain product produces significant improvement in precipitation amount and structure for Hurricane Harvey (2017) forecast. Since IMERG data is available half-hourly, further forecast improvement is expected with continuous assimilation of IMERG data

Precipitation↗

NASA NTRS2018

NASA SPoRT JPSS PG Activities in Alaska

SPoRT (NASA's Short-term Prediction Research and Transition Center) has collaboratively worked with Alaska WFOs (Weather Forecast Offices) to introduce RGB (Red/Green/Blue false color image) imagery to prepare for NOAA-20 (National Oceanic and Atmospheric Administration, JPSS (Joint Polar Satellite System) series-20 satellite) VIIRS (Visible Infrared Imaging Radiometer Suite) and improve forecasting aviation-related hazards. Last R2O/O2R (Research-to-Operations/Operations-to-Research) steps include incorporating NOAA-20 VIIRS in RGB suite and fully transitioning client-side RGB processing to GINA (Geographic Information Network of Alaska) and Alaska Region. Alaska Region WFOs have been part of the successful R2O/O2R story to assess the use of NESDIS (National Environmental Satellite, Data, and Information Service) Snowfall Rate product in operations. SPoRT introduced passive microwave rain rate and IMERG (Integrated Multi-satellitE Retrievals for GPM (Global Precipitation Measurement)) (IMERG) to Alaska WFOs for use in radar-void areas and assessing flooding potential. SPoRT has been part of the multi-organization collaborative effort to introduce Gridded NUCAPS (NOAA Unique CrIS/ATMS (Crosstrack Infrared Sounder/Advanced Technology Microwave Sounder) Processing System) to the Anchorage CWSU (Center Weather Service Unit) to assess Cold Air Aloft events, [and as part of NOAA's PG (Product Generation) effort].

Berndt, Emily↗

NASA NTRS2017

An Integrated Examination of AMSR2 Products over Ocean

Integrated processing and analysis are presented for oceanic retrievals of precipitation, sea ice concentration, columnar water vapor and cloud water, sea surface temperature, and near surface wind from the advanced microwave scanning radiometer-2 (AMSR2) sensor. By developing a common algorithmic framework and permitting iterative interaction between historically separate science algorithms, ambiguous and contradictory retrieval results are minimized. The integration also serves to improve each algorithm individually, decreasing reliance on ancillary datasets. Case studies are presented that exemplify both ongoing challenges for these retrievals and potential research uses of such an integrated satellite product. Two cases are presented each for precipitation at high latitudes and marginal sea ice detection, with analysis supplemented by satellite data from CloudSat profiles and Himawari-8 imagery. Detection and retrieval of snowfall remains a challenge, while light rainfall detection can be aided by a variational algorithm. Sea ice concentrations below 20% cause disagreements, but the algorithms otherwise agree well on the sea ice edge. Potential mitigation strategies for ambiguous areas of light rainfall and marginal sea ice are discussed. The analysis demonstrates potential avenues for future algorithm development, but also some physical limitations of remote sensing with the AMSR2 frequencies.

variational methods↗

NASA NTRS2017

Intercomparison of Snow Depth Retrievals over Arctic Sea Ice from Radar Data Acquired by Operation IceBridge

Since 2009, the ultra-wideband snow radar on Operation IceBridge (OIB; a NASA airborne mission to survey the polar ice covers) has acquired data in annual campaigns conducted during the Arctic and Antarctic springs. Progressive improvements in radar hardware and data processing methodologies have led to improved data quality for subsequent retrieval of snow depth. Existing retrieval algorithms differ in the way the air-snow (a-s) and snow-ice (s-i) interfaces are detected and localized in the radar returns and in how the system limitations are addressed (e.g., noise, resolution). In 2014, the Snow Thickness On Sea Ice Working Group (STOSIWG) was formed and tasked with investigating how radar data quality affects snow depth retrievals and how retrievals from the various algorithms differ. The goal is to understand the limitations of the estimates and to produce a well-documented, long-term record that can be used for understanding broader changes in the Arctic climate system. Here, we assess five retrieval algorithms by comparisons with field measurements from two groundbased campaigns, including the BRomine, Ozone, and Mercury EXperiment (BROMEX) at Barrow, Alaska; a field program by Environment and Climate Change Canada at Eureka, Nunavut; and available climatology and snowfall from ERA-Interim (ECMWF (European Centre for Medium-Range Weather Forecasts) Re-Analysis) reanalysis. The aim is to examine available algorithms and to use the assessment results to inform the development of future approaches. We present results from these assessments and highlight key considerations for the production of a long-term, calibrated geophysical record of springtime snow thickness over Arctic sea ice.

Operation IceBridge↗