A joint US Air Force/National Aeronautics and Space Administration (NASA) blended global snow product that uses Earth Observation System Moderate Resolution Imaging Spectroradiometer (MODIS), Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) and Quick Scatterometer (QuikSCAT or QSCAT) data has been developed. Existing snow products derived from these sensors have been blended into a single, global, daily, user-friendly product by using a newly developed Air Force Weather Agency (AFWA)/NASA Snow Algorithm (ANSA). This initial blended snow product uses minimal modelling to expeditiously yield improved snow products, which include, or will include, snow-cover extent, fractional snow cover, snow water equivalent (SWE), onset of snowmelt and identification of actively melting snow cover. The blended snow products are currently 25-km resolution. These products are validated with data from the lower Great Lakes region of the USA, from Colorado obtained during the Cold Land Processes Experiment (CLPX), and from Finland. The AMSR-E product is especially useful in detecting snow through clouds; however, passive microwave data miss snow in those regions where the snow cover is thin, along the margins of the continental snowline, and on the lee side of the Rocky Mountains, for instance. In these regions, the MODIS product can map shallow snow cover under cloud-free conditions. The confidence for mapping snow-cover extent is greater with the MODIS product than with the microwave product when cloud-free MODIS observations are available. Therefore, the MODIS product is used as the default for detecting snow cover. The passive microwave product is used as the default only in those areas where MODIS data are not applicable due to the presence of clouds and darkness. The AMSR-E snow product is used in association with the difference between ascending and descending satellite passes or diurnal-amplitude variations (DAV) to detect the onset of melt, and a QSCAT product will be used to map areas of snow that are actively melting.

A blended global snow product using visible, passive microwave and scatterometer satellite data

[1] North Atlantic variability in general, and the North Atlantic Oscillation (NAO) in particular, is a long-studied, very important but still not well-understood problem in climatology. The recent trend to a higher wintertime NAO index was accompanied by an additional increase in the Azores High not coupled to changes in the Icelandic Low, as shown by a self-organizing maps (SOMs) analysis of monthly mean DJF mean sea level pressure data from 1957 to 2002. SOMs are a nonlinear tool to optimally extract a user-specified number of patterns or icons from an input data set and to uniquely relate any input data field to an icon, allowing analyses of occurrence frequencies and transitions complementary to principal component analysis (PCA). SOMs analysis of ERA-40 data finds a North Atlantic ‘‘monopole’’ roughly colocated with the mean position of the Azores High, as well as the well-known NAO dipole involving the Icelandic Low and the subtropical high. Little trend is shown in December, but the Azores High increased along with the NAO in January and February over the study interval, with implications for storminess in northwestern Europe. In short, our SOM-based analyses of winter MSLP have both confirmed prior knowledge and expanded it through the relative ease of use and power with nonlinear systems of the SOM-based approach to climatological analysis.

/pdf/north-atlantic-climate-variability-from-a-self-organizing-2h82qx93dl.pdf

North Atlantic climate variability from a self-organizing map perspective

[1] This paper describes research conducted to develop an integrated snow monitoring algorithm at global and regional scales for the Special Sensor Microwave Imager (SSM/I) and the planned Advanced Microwave Scanning Radiometer-EOS. Methods to detect global snow cover are well advanced and have been applied routinely at local, regional, and global scales. Current snow cover retrieval methods tend to underestimate snow volume because important fractional forest cover and snowpack physical properties are not well parameterized in simple snow parameter retrieval schemes. Building on a static approach to snow depth retrieval, a spatially dynamic algorithm is described that incorporates information about fractional forest cover and snowpack physical development. SSM/I brightness temperatures of land surfaces are calibrated with ground measurements of snow depth through empirical statistical and geostatistical models to produce globally distributed snow depth estimates. The calibration is based on a 4-year and a one winter season record of daily snow depth and SSM/I observations. The biasing effect of forest cover on microwave estimates is quantified and short-term fluctuations of microwave-estimated snow depth (caused by physical changes in the pack) are reduced by time smoothing daily estimated snow depth values. For the data sets used the estimated snow depth from the new algorithm gives global seasonal errors of 13.7 and 22.1 cm for the two data sets. Further major improvements in snow depth retrieval methods will only be achieved through enhancements to forest and snow metamorphism parameterizations.

https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2002RS002648

Development of a passive microwave global snow depth retrieval algorithm for Special Sensor Microwave Imager (SSM/I) and Advanced Microwave Scanning Radiometer-EOS (AMSR-E) data

[1] Arctic sea ice plays a key role in the climate system, by acting as the interface between a warm ocean and a cold atmosphere. Establishing the true pattern of recent behavior of the sea ice in this region is critical to simulating the role of sea ice in future climate. Recently released operational ice charts from the U.S. National Ice Center provide insight into the late twentieth century behavior of Northern Hemisphere sea ice, providing more reliable ice concentrations during summer and freeze-up than those available from the passive microwave record. The major winter and summer modes of ice concentration variability observed from empirical orthogonal function analysis covering the period 1972–1994 are shown to indicate, respectively, the 1-year lagged response of the sea ice to the North Atlantic Oscillation and the winter preconditioning of summer sea ice coverage in the eastern Arctic by the North Atlantic Oscillation. Feedback to the atmosphere is suggested in each case by zero-lag cyclone frequency relationships to these two sea ice modes of variability.

/pdf/late-twentieth-century-northern-hemisphere-sea-ice-record-nww4l5bbjn.pdf

Late twentieth century Northern Hemisphere sea‐ice record from U.S. National Ice Center ice charts

The Earth’s snow and sea ice cover are among the Earth’s most dynamic features. Over 40% of the Earth’s land surface may be covered by snow cover during the Northern Hemisphere winter, and sea ice covers 5%–8% of the ocean surface at any given time. Both snow and sea ice covers act as an effective insulating layer between the atmosphere and land or ocean surface because the low thermal conductivity of snow and sea ice restricts exchanges of heat and chemical constituents between the atmosphere and the land or ocean.

/pdf/modis-snow-and-sea-ice-products-1l61jowsll.pdf

MODIS Snow and Sea Ice Products

The results of simultaneous observations of charged particle fluxes within the energy range 50 eV to 20 keV (plasma energy-angle spectrometer, PEAS experiment) and plasma waves within the frequency range 0.1 to 10 MHz (plasma radio spectrometer, PRS 3 experiment) on board the APEX spacecraft are presented. The data were obtained at polar and auroral latitudes in the dawn-dusk and noon-midnight time sectors. The low-frequency (LF) sporadic emission mainly on frequencies lower than the local gyrofrequency was commonly observed within a comparatively narrow latitude interval (∼5°– 6°) of the auroral oval. The maximum of spectral wave intensity was revealed at a frequency of ∼ 200 kHz. The sporadic character of the observed emissions, the spectra, are very similar to those reported for auroral kilometric radiation (AKR), and the temporal and spatial occurrences imply its relation to the source of AKR emissions measured at the higher altitudes, with regards to our wave measurements as LF AKR-type emissions. Comparative analysis of spatial distributions of charged particle energy spectra and variations of wave spectra at auroral oval latitudes showed that several conditions should be fulfilled for LF AKR generation. These conditions are related both to the intensity and energy of precipitating ion and electron fluxes, and to albedo electrons and the ionospheric plasma parameters. The wideband HF emission was observed together with the LF AKR-type emission during the morning auroral oval crossing. At spacecraft apogee altitudes, in the polar cap zone intense electron precipitations and an increase of plasma noise intensity at frequencies equal or below ƒHe were observed at the period when the IMF was northward These precipitations and emissions were accompanied by the horizontal currents crossing the polar cap, the so-called theta structure.

https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/96JA01116

The charged-particle fluxes at auroral and polar latitudes and related low-frequency auroral kilometric radiation-type and high-frequency wideband emission

Broadband hf emissions as an indicator of global changes within the ionosphere

Plasma Processes in the Region of Electron Beam Injection from a High-Altitude Payload

H.F. emission of one-dimensional electron beam emitted from body subject to neutralisation current

Broad-band hectometric emission in the topside ionosphere created by ground-based transmitters

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Z. Klos

Author Tools

Chat about Author