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NASA NTRS2017

Contribution of Lake-Effect Snow to the Catskill Mountains Snowpack

Meltwater from snow that falls in the Catskill Mountains in southern New York contributes to reservoirs that supply drinking water to approximately nine million people in New York City. Using the NOAA National Ice Centers Interactive Multisensor Snow and Ice Mapping System (IMS) 4km snow maps, we have identified at least 32 lake-effect (LE) storms emanating from Lake Erie andor Lake Ontario that deposited snow in the CatskillDelaware Watershed in the Catskill Mountains of southern New York State between 2004 and 2017. This represents a large underestimate of the contribution of LE snow to the Catskills snowpack because many of the LE snowstorms are not visible in the IMS snow maps when they travel over snow-covered terrain. Most of the LE snowstorms that we identified originate from Lake Ontario but quite a few originate from both Erie and Ontario, and a few from Lake Erie alone. Using satellite, meteorological and reanalysis data we identify conditions that contributed to LE snowfall in the Catskills. Clear skies following some of the storms permitted measurement of the extent of snow cover in the watershed using multiple satellite sensors. IMS maps tend to overestimate the extent of snow compared to MODerate resolution Imaging Spectroradiometer (MODIS) and Landsat-derived snow-cover extent maps. Using this combination of satellite and meteorological data, we can begin to quantify the important contribution of LE snow to the Catskills Mountain snowpack. Changes that are predicted in LE snowfall from the Great Lakes could impact the distribution of rain vs snow in the Catskills which may affect future reservoir operations in the NYC Water Supply System.

Lake Ontario↗

NASA NTRS2017

Polycrystal Snowfakes

Building on the previous success of a mono-crystal snowflake growth model, a team of NASA researchers has invented and implemented a poly-crystal variant of the model to simulate simultaneous crystalline growth along lattices of different orientations. Inferring snowfall intensity and amount using remote sensing relies on knowing the scattering properties of snowing particles. However, these scattering properties depend strongly on the geometric shapes of the snowing particles. Poly-crystal snow particles, such as bullet rosettes, appear frequently in nature. Simulating the growth of both mono-crystal and poly-crystal particles and obtaining accurate scattering properties are paramount for furthering accurate quantitative estimates of snowfalls.

Clune, Tom↗

NASA NTRS2017

Reconciling CloudSat and GPM Estimates of Falling Snow

Satellite-based estimates of falling snow have been provided by CloudSat (launched in 2006) and the Global Precipitation Measurement (GPM) core satellite (launched in 2014). The CloudSat estimates are derived from W-band radar measurements whereas the GPM estimates are derived from its scanning Ku- and Ka-band Dual-Frequency Precipitation Radar (DPR) and 13-channel microwave imager (GMI). Each platform has advantages and disadvantages: CloudSat has higher resolution (approximately 1.5 km) and much better sensitivity (-28 dBZ), but poorer sampling (nadir-only and daytime-only since 2011) and the reflectivity-snowfall (Z-S) relationship is poorly constrained with single-frequency measurements. Meanwhile, DPR suffers from relatively poor resolution (5 km) and sensitivity (approximately 13 dBZ), but has cross-track scanning capability to cover a 245-km swath. Additionally, where Ku and Ka measurements are available, the conversion of reflectivity to snowfall rate is better-constrained than with a single frequency.

S Joseph Munchak↗

NASA NTRS2017

Chapter 7: Precipitation Change in the United States

Annual precipitation has decreased in much of the West, Southwest, and Southeast and increased in most of the Northern and Southern Plains, Midwest, and Northeast. A national average increase of 4% in annual precipitation since 1901 is mostly a result of large increases in the fall season. Heavy precipitation events in most parts of the United States have increased in both intensity and frequency since 1901. There are important regional differences in trends, with the largest increases occurring in the northeastern United States. In particular, mesoscale convective systems (organized clusters of thunderstorms)-the main mechanism for warm season precipitation in the central part of the United States-have increased in occurrence and precipitation amounts since 1979. The frequency and intensity of heavy precipitation events are projected to continue to increase over the 21st century (high confidence). Mesoscale convective systems in the central United States are expected to continue to increase in number and intensity in the future. There are, however, important regional and seasonal differences in projected changes in total precipitation: the northern United States, including Alaska, is projected to receive more precipitation in the winter and spring, and parts of the southwestern United States are projected to receive less precipitation in the winter and spring. Northern Hemisphere spring snow cover extent, North America maximum snow depth, snow water equivalent in the western United States, and extreme snowfall years in the southern and western United States have all declined, while extreme snowfall years in parts of the northern United States have increased. Projections indicate large declines in snowpack in the western United States and shifts to more precipitation falling as rain than snow in the cold season in many parts of the central and eastern United States.

Easterling, D. R.↗

NASA NTRS2019

Turning Satellite Data into Global Precipitation Maps

It's really obvious how to measure rain and snowfall just put a container out in the yard. But, it turns out the physical processes that create precipitation, and the practicalities of taking measurements everywhere, make satellites key tools in mapping rain and snowfall around the globe. Employing satellites in this meteorological detective story involves some science, some technology, a lot of computing, and, yes, rocket science. The payoff is that we know more about how much rain and snow fell in, say, 2010, than we knew at the time that it fell, and we're ready to make the same kind of estimate for tomorrow's storms. There is still a lot of work to be done to make these estimates more reliable and accurate.Dr. George J. Huffman is a Research Meteorologist at NASA Goddard Space Flight Center (GSFC), Greenbelt, Maryland. After completing a B.S. in Physics at The Ohio State University (1976) and a Ph.D. in Meteorology at Massachusetts Institute of Technology (1982), he was an Assistant Professor at University of Maryland, College Park before moving to GSFC in 1988, where he consulted for 24 years before joining NASA in 2012. Dr. Huffman focuses on the design, implementation, and extension of combined estimates of global precipitation, seeking to incorporate all available precipitation-relevant satellite and global surface data. He is the Deputy Project Scientist for the Global Precipitation Measurement (GPM) mission, the Chair of the GPM Multi-satellite Algorithm Working Group, and Chief of the Mesoscale Atmospheric Processes Lab.

Huffman, George J.↗

NASA NTRS2020

Unprecedented Atmospheric Conditions (1948–2019) Drive the 2019 Exceptional Melting Season Over the Greenland Ice Sheet

Understanding the role of atmospheric circulation anomalies on the surface mass balance of the Greenland ice sheet (GrIS) is fundamental for improving estimates of its current and future contributions to sea level rise. Here, we show, using a combination of remote sensing observations, regional climate model outputs, reanalysis data, and artificial neural networks, that unprecedented atmospheric conditions (1948–2019) occurring in the summer of 2019 over Greenland promoted new record or close-to-record values of surface mass balance (SMB), runoff, and snowfall. Specifically, runoff in 2019 ranked second within the 1948–2019 period (after 2012) and first in terms of surface mass balance negative anomaly for the hydrological year 1 September 2018–31 August 2019. The summer of 2019 was characterized by an exceptional persistence of anticyclonic conditions that, in conjunction with low albedo associated with reduced snowfall in summer, enhanced the melt–albedo feedback by promoting the absorption of solar radiation and favored advection of warm, moist air along the western portion of the ice sheet towards the north, where the surface melt has been the highest since 1948. The analysis of the frequency of daily 500 hPa geopotential heights obtained from artificial neural networks shows that the total number of days with the five most frequent atmospheric patterns that characterized the summer of 2019 was 5 standard deviations above the 1981–2010 mean, confirming the exceptional nature of the 2019 season over Greenland.

Greenland ice sheet↗

NASA NTRS2020

Arctic Snow Depth and Sea Ice Thickness From ICESat‐2 and CryoSat‐2 Freeboards: A First Examination

We present a first examination of Arctic sea ice snow depth estimates from differencing satellite lidar (ICESat‐2) and radar (CryoSat‐2) freeboards. These estimates cover the period between 14 October 2018 and the end of April 2019. Snow depth is related to freeboard differences by the refractive index/bulk density of the snow layer—the only free parameter in the approach. Area‐averaged snow depth ranges from 9 cm (on first‐year ice: 5 cm, multiyear ice: 14 cm) in late October to 19 cm (first‐year ice: 17 cm, multiyear ice: 27 cm) in April; on average, this snow is thinner over FYI. Spatial patterns and gradients of snow depth estimates compare well with reconstructions using snowfall from ERA‐Interim and ERA5, although snowfall from ERA5 is systematically higher. For all months, the results suggest that ~50% of the total freeboard is comprised of snow. Retrievals are within a few centimeters of snow depth data acquired by Operation IceBridge in April 2019. Sources of uncertainties associated with this freeboard‐differencing approach are discussed. Further, sea ice thicknesses calculated using the retrieved snow depth and a modified climatology are contrasted. Comparatively, the snow depth and calculated ice thickness using a modified climatology are higher by ~5 cm and 0.33 m, although these differences are not uniform throughout the season. Snow accumulation was slower between October and December but increased between December and January, unlike the modified climatology, which exhibited a monotonic accumulation for all months. Future opportunities for assessment and improvement of these estimates are discussed.

Arctic Ocean↗

NASA NTRS2020

The Precipitation Imaging Package: Assessment of Microphysical and Bulk Characteristics of Snow

Remote-sensing observations are needed to estimate the regional and global impacts of snow. However, to retrieve accurate estimates of snow mass and rate, these observations require augmentation through additional information and assumptions about hydrometeor properties. The Precipitation Imaging Package (PIP) provides information about precipitation characteristics and can be utilized to improve estimates of snowfall rate and accumulation. Here, the goal is to demonstrate the quality and utility of two higher-order PIP-derived products: liquid water equivalent snow rate and an approximation of volume-weighted density called equivalent density. Accuracy of the PIP snow rate and equivalent density is obtained through intercomparison with established retrieval methods and through evaluation with colocated ground-based observations. The results confirm the ability of the PIP-derived products to quantify properties of snow rate and equivalent density, and demonstrate that the PIP produces physically realistic snow characteristics. When compared to the National Weather Service (NWS) snow field measurements of six-hourly accumulation, the PIP-derived accumulations were biased only +2.48% higher. Additionally, this work illustrates fundamentally different microphysical and bulk features of low and high snow-to-liquid ratio events, through assessment of observed particle size distributions, retrieved mass coefficients, and bulk properties. Importantly, this research establishes the role that PIP observations and higher-order products can serve for constraining microphysical assumptions in ground-based and spaceborne remotely sensed snowfall retrievals.

Claire Pettersen↗

NASA NTRS2022

Tracking the Impacts of Precipitation Phase Changes Through the Hydrologic Cycle in Snowy Regions: From Precipitation to Reservoir Storage

Cool season precipitation plays a critical role in regional water resource management in the western United States. Throughout the twenty-first century, regional precipitation will be impacted by rising temperatures and changing circulation patterns. Changes to precipitation magnitude remain challenging to project; however, precipitation phase is largely dependent on temperature, and temperature predictions from global climate models are generally in agreement. To understand the implications of this dependence, we investigate projected patterns in changing precipitation phase for mountain areas of the western United States over the twenty-first century and how shifts from snow to rain may impact runoff. We downscale two bias-corrected global climate models for historical and end-century decades with the Weather Research and Forecasting (WRF) regional climate model to estimate precipitation phase and spatial patterns at high spatial resolution (9 km). For future decades, we use the RCP 8.5 scenario, which may be considered a very high baseline emissions scenario to quantify snow season differences over major mountain chains in the western U.S. Under this scenario, the average annual snowfall fraction over the Sierra Nevada decreases by >45% by the end of the century. In contrast, for the colder Rocky Mountains, the snowfall fraction decreases by 29%. Streamflow peaks in basins draining the Sierra Nevada are projected to arrive nearly a month earlier by the end of the century. By coupling WRF with a water resources model, we estimate that California reservoirs will shift towards earlier maximum storage by 1–2 months, suggesting that water management strategies will need to adapt to changes in streamflow magnitude and timing.

Melissa L. Wrzesien↗

NASA NTRSDate not supplied

A LaGrangian Perspective of the Surface Energy Budget Contributions to Sea Ice Evolution in the ‘New Arctic’

Recent Arctic warming outpaces global warming and is characterized by dramatic changes in sea ice (record sea ice loss, thinner and more seasonal ice cover), warming SSTs, and a lengthening melt season. These changes impact the surface energy budget and the sensitivity of sea ice to radiative forcing, namely an increased sensitivity of thinner ice to local radiative forcings and a weaker buffering of surface-atmosphere exchanges of heat and moisture. However, these processes are a large source of uncertainty in global climate models with prior studies finding that poor representations of downwelling longwave radiation and sea ice albedo lead to model spread in sea ice conditions. A limitation of prior work is examining sea ice-atmosphere interactions through an Eulerian framework. This study utilizes a sea ice parcel dataset in a LaGrangian framework, allowing for tracking of surface energy budget-sea ice interactions over the lifespan of individual sea ice parcels; this framework also allows for the quantification of the sea ice response to episodic weather events along with seasonal drivers, including the drivers of rapid growth and melt events. First, lag-lead relationships between relevant surface energy budget terms and sea ice thickness changes are explored to understand the atmospheric conditions preceding rapid growth and melt. The impact of episodic events- moisture intrusion, warm air outbreaks, cyclones- on sea ice growth are also investigated by stratifying first-year parcels by sea ice thickness at the end of the growth season and analyzing the effect of atmospheric variability (number and intensity of episodic events) on parcel thickness change. Lastly, parcel growth and melt and the associated radiative anomalies are compared to snowfall events/snow thickness to determine how snowfall modifies the interaction between the local surface energy budget and sea ice mass balance.

Patrick Taylor↗

NASA NTRS2021

Chapter 6: Satellite Rainfall Estimation

The measurement of precipitation (rainfall and snowfall) across the globe is important for a variety of scientific and social applications. Knowledge of the occurrence and amount of precipitation, together with its distribution and changes are crucial for improving our understanding of the global energy and water cycle, as well as for monitoring water resource availability and for hydrological modelling to help alleviate flood and drought impacts. The use of conventional instruments (gauge or radar) to map global precipitation is essentially limited to land areas and thus satellite observations must be used to provide estimates of global precipitation. Many satellite sensors operating over the last 50 years have provided data for a range of techniques, algorithms and schemes developed to generate quantitative precipitation estimates. Current satellite-based precipitation products can provide estimates at up to 4 km every 30 minutes. This chapter outlines the basis of satellite precipitation estimation, the satellites and sensors used, and the range of techniques and schemes used to generate the precipitation products.

Earth observation↗

NASA NTRS2021

Chapter 10: Development of a Precipitation Retrieval Scheme for Cross-track Passive Microwave Sounding Instruments

The measurement of precipitation (snowfall and rainfall) across the globe is of great importance to both science and society. Despite the importance, measurements from surface-based instruments are limited in extent and are generally absent from sparsely populated or inhospitable regions, including over the world’s oceans. Satellite-based observations have the potential to provide the necessary information to generate estimates of precipitation across the whole of the Earth’s surface. However, adequate temporal and spatial sampling is required to capture vagaries of precipitation and it is necessary to exploit all possible satellite (and, where appropriate surface) information. While much research has been carried out into the retrieval of precipitation from observations gathered by conically-scanning passive microwave imagers in previous decades, relatively little has been done to fully utilise the observations from the cross-track scanning passive microwave sounding instruments. This paper will review a number of cross-track precipitation retrieval schemes, together an evaluation of their performance, and future directions.

Precipitation retrievals↗

NASA NTRS2021

The Climates of Earth's Next Supercontinent: Effects of Tectonics, Rotation Rate, and Insolation

We explore two possible Earth climate scenarios, 200 and 250 million years into the future, using projections of the evolution of plate tectonics, solar luminosity, and rotation rate. In one scenario, a supercontinent forms at low latitudes, whereas in the other it forms at high northern latitudes with an Antarctic subcontinent remaining at the south pole. The climates between these two end points are quite stark, with differences in mean surface temperatures approaching several degrees. The main factor in these differences is related to the topographic height of the high latitude supercontinents where higher elevations promote snowfall and subsequent higher planetary albedos. These results demonstrate the need to consider multiple boundary conditions when simulating Earth-like exoplanetary climates.

insolation↗

NASA NTRS2023

Sensitivity of the MAR Regional Climate Model Snowpack to the Parameterization of the Assimilation of Satellite-Derived Wet-Snow Masks on the Antarctic Peninsula

Both regional climate models (RCMs) and remote sensing (RS) data are essential tools in understanding the response of polar regions to climate change. RCMs can simulate how certain climate variables, such as surface melt, runoff and snowfall, are likely to change in response to different climate scenarios but are subject to biases and errors. RS data can assist in reducing and quantifying model uncertainties by providing indirect observations of the modeled variables on the present climate. In this work, we improve on an existing scheme to assimilate RS wet snow occurrence data with the “Modèle Atmosphérique Régional” (MAR) RCM and investigate the sensitivity of the RCM to the parameters of the scheme. The assimilation is performed by nudging the MAR snowpack temperature to match the presence of liquid water observed by satellites. The sensitivity of the assimilation method is tested by modifying parameters such as the depth to which the MAR snowpack is warmed or cooled, the quantity of water required to qualify a MAR pixel as “wet” (0.1 % or 0.2 % of the snowpack mass being water), and assimilating different RS datasets. Data assimilation is carried out on the Antarctic Peninsula for the 2019–2021 period. The results show an increase in meltwater production (+66.7 % on average, or +95 Gt), along with a small decrease in surface mass balance (SMB) (−4.5 % on average, or −20 Gt) for the 2019–2020 melt season after assimilation. The model is sensitive to the tested parameters, albeit with varying orders of magnitude. The prescribed warming depth has a larger impact on the resulting surface melt production than the liquid water content (LWC) threshold due to strong refreezing occurring within the top layers of the snowpack. The values tested for the LWC threshold are lower than the LWC for typical melt days (approximately 1.2 %) and impact results mainly at the beginning and end of the melting period. The assimilation method will allow for the estimation of uncertainty in MAR meltwater production and will enable the identification of potential issues in modeling near-surface snowpack processes, paving the way for more accurate simulations of snow processes in model projections.

regional climate models↗

NASA NTRS1976

Meteorological interpretations of the images from the Nimbus 5 electrically scanned microwave radiometer

The Nimbus 5 Electrically Scanned Microwave Radiometer (ESMR) is useful in determining the extent, structure, and intensity of rainfall over oceans. It is possible on the basis of rainfall maps to determine the location of frontal rain, rain/snow boundaries, and the structure of tropical storms. ESMR data on rainfall over the east coast of the United States and the Gulf of Mexico, on snowfall over the northeast coast of the United States, on frontal zones in the northwestern Pacific and on the tropical storm Leila in the South Indian Ocean are examined. Because of the generally high emissivities of land surfaces, interpretation of meteorological parameters over land is not yet possible.

Wilheit, T. T.↗

NASA NTRS1977

Application of LANDSAT data to delimitation of avalanche hazards in Montane Colorado

The author has identified the following significant results. Many avalanche hazard zones can be identified on LANDSAT imagery, but not consistently over a large region. Therefore, regional avalanche hazard mapping, using LANDSAT imagery, must draw on additional sources of information. A method was devised that depicts three levels of avalanche hazards according to three corresponding levels of certainty that active avalanches occur. The lowest level, potential avalanche hazards, was defined by delineating slopes steep enough to support avalanches at elevations where snowfall was likely to be sufficient to produce a thick snowpack. The intermediate level of avalanche hazard was interpreted as avalanche hazard zones. These zones have direct and indirect indicators of active avalanche activity and were interpreted from LANDSAT imagery. The highest level of known or active avalanche hazards was compiled from existing maps. Some landslides in Colorado were identified and, to a degree, delimited on LANDSAT imagery, but the conditions of their identification were highly variable. Because of local topographic, geologic, structural, and vegetational variations, there was no unique landslide spectral appearance.

Knepper, D. H., Jr.↗

NASA NTRS1981

The President's Day cyclone 17-19 February 1979: An analysis of jet streak interactions prior to cyclogenesis

The President's Day cyclone, produced record breaking snowfall along the East Coast of the United States in February 1979. Conventional radiosonde data, SMS GOES infrared imagery and LFM 2 model diagnostics were used to analyze the interaction of upper and lower tropospheric jet streaks prior to cyclogenesis. The analysis reveals that a series of complex scale interactive processes is responsible for the development of the intense cyclone. The evolution of the subsynoptic scale mass and momentum fields prior to and during the period of rapid development of the President's Day cyclone utilizing conventional data and SMS GOES imagery is documented. The interaction between upper and lower tropospheric jet streaks which occurred prior to the onset of cyclogenesis is discussed as well as the possible effects of terrain modified airflow within the precyclogenesis environment. Possible deficiencies in the LFM-2 initial wind fields that could have been responsible, in part, for the poor numerical forecast are examined.

Uccellini, L. W.↗

NASA NTRS1983

An analysis of the 'Blizzard of '88'

The analysis of the blizzard, an intense cyclone that was accompanied by unusually heavy snowfall, high winds, and cold temperatures, is carried out using a collection of detailed surface weather observations. It follows the cyclone from its genesis along a slow-moving frontal system through its rapid development and occlusion along the Middle Atlantic and southern New England coasts. Unusual aspects of the cyclone are discussed. Among these are the limited areal extent of heavy snow accumulations, the establishment of very cold air across western New England and the Middle Atlantic states, a persistent stationary front zone across central New England that separated frigid continental air from maritime air, and the slow movement and rapid warming associated with the decay of the storm.

Kocin, P. J.↗