Evolution of Melt Pond Fraction and Depth on Multiyear Ice in 2020 from High Resolution Satellite Observations

Abstract. Accurate representations of mean climate conditions, especially in areas of complex terrain, are an important part of environmental monitoring systems. As high-resolution satellite monitoring information accumulates with the passage of time, it can be increasingly useful in efforts to better characterize the earth's mean climatology. Current state-of-the-science products rely on complex and sometimes unreliable relationships between elevation and station-based precipitation records, which can result in poor performance in food and water insecure regions with sparse observation networks. These vulnerable areas (like Ethiopia, Afghanistan, or Haiti) are often the critical regions for humanitarian drought monitoring. Here, we show that long period of record geo-synchronous and polar-orbiting satellite observations provide a unique new resource for producing high-resolution (0.05°) global precipitation climatologies that perform reasonably well in data-sparse regions. Traditionally, global climatologies have been produced by combining station observations and physiographic predictors like latitude, longitude, elevation, and slope. While such approaches can work well, especially in areas with reasonably dense observation networks, the fundamental relationship between physiographic variables and the target climate variables can often be indirect and spatially complex. Infrared and microwave satellite observations, on the other hand, directly monitor the earth's energy emissions. These emissions often correspond physically with the location and intensity of precipitation. We show that these relationships provide a good basis for building global climatologies. We also introduce a new geospatial modeling approach based on moving window regressions and inverse distance weighting interpolation. This approach combines satellite fields, gridded physiographic indicators, and in situ climate normals. The resulting global 0.05° monthly precipitation climatology, the Climate Hazards Group's Precipitation Climatology version 1 (CHPclim v.1.0, doi:10.15780/G2159X), is shown to compare favorably with similar global climatology products, especially in areas with complex terrain and low station densities.

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Melt Pond Retrieval Based on the LinearPolar Algorithm Using Landsat Data

Remote Sensing ◽

10.3390/rs13224674 ◽

2021 ◽

Vol 13 (22) ◽

pp. 4674

Author(s):

Yuqing Qin ◽

Jie Su ◽

Mingfeng Wang

Keyword(s):

Remote Sensing ◽

High Resolution ◽

Relative Error ◽

Large Scale ◽

The Arctic ◽

Landsat Data ◽

Melt Pond ◽

Melt Ponds ◽

Very High ◽

Sentinel 2

The formation and distribution of melt ponds have an important influence on the Arctic climate. Therefore, it is necessary to obtain more accurate information on melt ponds on Arctic sea ice by remote sensing. The present large-scale melt pond products, especially the melt pond fraction (MPF), still require verification, and using very high resolution optical satellite remote sensing data is a good way to verify the large-scale retrieval of MPF products. Unlike most MPF algorithms using very high resolution data, the LinearPolar algorithm using Sentinel-2 data considers the albedo of melt ponds unfixed. In this paper, by selecting the best band combination, we applied this algorithm to Landsat 8 (L8) data. Moreover, Sentinel-2 data, as well as support vector machine (SVM) and iterative self-organizing data analysis technique (ISODATA) algorithms, are used as the comparison and verification data. The results show that the recognition accuracy of the LinearPolar algorithm for melt ponds is higher than that of previous algorithms. The overall accuracy and kappa coefficient results achieved by using the LinearPolar algorithm with L8 and Sentinel-2A (S2), the SVM algorithm, and the ISODATA algorithm are 95.38% and 0.88, 94.73% and 0.86, and 92.40%and 0.80, respectively, which are much higher than those of principal component analysis (PCA) and Markus algorithms. The mean MPF (10.0%) obtained from 80 cases from L8 data based on the LinearPolar algorithm is much closer to Sentinel-2 (10.9%) than the Markus (5.0%) and PCA algorithms (4.2%), with a mean MPF difference of only 0.9%, and the correlation coefficients of the two MPFs are as high as 0.95. The overall relative error of the LinearPolar algorithm is 53.5% and 46.4% lower than that of the Markus and PCA algorithms, respectively, and the root mean square error (RMSE) is 30.9% and 27.4% lower than that of the Markus and PCA algorithms, respectively. In the cases without obvious melt ponds, the relative error is reduced more than that of those with obvious melt ponds because the LinearPolar algorithm can identify 100% of dark melt ponds and relatively small melt ponds, and the latter contributes more to the reduction in the relative error of MPF retrieval. With a wider range and longer time series, the MPF from Landsat data are more efficient than those from Sentinel-2 for verifying large-scale MPF products or obtaining long-term monitoring of a fixed area.

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EnOI-Based Data Assimilation Technology for Satellite Observations and ARGO Float Measurements in a High Resolution Global Ocean Model Using the CMF Platform

Communications in Computer and Information Science - Supercomputing ◽

10.1007/978-3-319-55669-7_5 ◽

2016 ◽

pp. 57-66 ◽

Cited By ~ 4

Author(s):

Maxim Kaurkin ◽

Rashit Ibrayev ◽

Alexandr Koromyslov

Keyword(s):

High Resolution ◽

Data Assimilation ◽

Ocean Model ◽

Satellite Observations ◽

Global Ocean ◽

Argo Float

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Monitoring evolution of melt ponds on first-year and multiyear sea ice in the Canadian Arctic Archipelago with optical satellite data

Annals of Glaciology ◽

10.1017/aog.2020.24 ◽

2020 ◽

Vol 61 (82) ◽

pp. 154-163

Author(s):

Qing Li ◽

Chunxia Zhou ◽

Lei Zheng ◽

Tingting Liu ◽

Xiaotong Yang

Keyword(s):

Sea Ice ◽

Canadian Arctic ◽

Arctic Sea Ice ◽

First Year ◽

Landsat 8 ◽

Canadian Arctic Archipelago ◽

Melt Pond ◽

Melt Ponds ◽

Validation Experiment ◽

Multiyear Ice

AbstractThe evolution of melt ponds on Arctic sea ice in summer is one of the main factors that affect sea-ice albedo and hence the polar climate system. Due to the different spectral properties of open water, melt pond and sea ice, the melt pond fraction (MPF) can be retrieved using a fully constrained least-squares algorithm, which shows a high accuracy with root mean square error ~0.06 based on the validation experiment using WorldView-2 image. In this study, the evolution of ponds on first-year and multiyear ice in the Canadian Arctic Archipelago was compared based on Sentinel-2 and Landsat 8 images. The relationships of pond coverage with air temperature and albedo were analysed. The results show that the pond coverage on first-year ice changed dramatically with seasonal maximum of 54%, whereas that on multiyear ice changed relatively flat with only 30% during the entire melting period. During the stage of pond formation, the ponds expanded rapidly when the temperature increased to over 0°C for three consecutive days. Sea-ice albedo shows a significantly negative correlation (R = −1) with the MPF in melt season and increases gradually with the refreezing of ponds and sea ice.

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How Machine Learning and High‐Resolution Imagery Can Improve Melt Pond Retrieval From MODIS Over Current Spectral Unmixing Techniques

Journal of Geophysical Research Oceans ◽

10.1029/2019jc015569 ◽

2020 ◽

Vol 125 (2) ◽

Cited By ~ 1

Author(s):

Nicholas C. Wright ◽

Chris M. Polashenski

Keyword(s):

Machine Learning ◽

High Resolution ◽

Spectral Unmixing ◽

Melt Pond ◽

High Resolution Imagery

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Adequacy of the In Situ Observing System in the Satellite Era for Climate SST

Journal of Atmospheric and Oceanic Technology ◽

10.1175/jtech1828.1 ◽

2006 ◽

Vol 23 (1) ◽

pp. 107-120 ◽

Cited By ~ 10

Author(s):

Huai-Min Zhang ◽

Richard W. Reynolds ◽

Thomas M. Smith

Keyword(s):

High Resolution ◽

Satellite Observations ◽

Sea Surface ◽

Bias Error ◽

Random Errors ◽

In Situ Data ◽

Satellite Sst ◽

Maximum Bias ◽

Very High

Abstract A method is presented to evaluate the adequacy of the recent in situ network for climate sea surface temperature (SST) analyses using both in situ and satellite observations. Satellite observations provide superior spatiotemporal coverage, but with biases; in situ data are needed to correct the satellite biases. Recent NOAA/U.S. Navy operational Advanced Very High Resolution Radiometer (AVHRR) satellite SST biases were analyzed to extract typical bias patterns and scales. Occasional biases of 2°C were found during large volcano eruptions and near the end of the satellite instruments’ lifetime. Because future biases could not be predicted, the in situ network was designed to reduce the large biases that have occurred to a required accuracy. Simulations with different buoy density were used to examine their ability to correct the satellite biases and to define the residual bias as a potential satellite bias error (PSBE). The PSBE and buoy density (BD) relationship was found to be nearly exponential, resulting in an optimal BD range of 2–3 per 10° × 10° box for efficient PSBE reduction. A BD of two buoys per 10° × 10° box reduces a 2°C maximum bias to below 0.5°C and reduces a 1°C maximum bias to about 0.3°C. The present in situ SST observing system was evaluated to define an equivalent buoy density (EBD), allowing ships to be used along with buoys according to their random errors. Seasonally averaged monthly EBD maps were computed to determine where additional buoys are needed for future deployments. Additionally, a PSBE was computed from the present EBD to assess the in situ system’s adequacy to remove potential future satellite biases.

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A very high-resolution global fossil fuel CO<sub>2</sub> emission inventory derived using a point source database and satellite observations of nighttime lights, 1980–2007

Atmospheric Chemistry and Physics Discussions ◽

10.5194/acpd-10-16307-2010 ◽

2010 ◽

Vol 10 (7) ◽

pp. 16307-16344 ◽

Cited By ~ 13

Author(s):

T. Oda ◽

S. Maksyutov

Keyword(s):

High Resolution ◽

Point Source ◽

Emission Inventory ◽

Fossil Fuel ◽

Population Distribution ◽

Population Based ◽

Point Sources ◽

Satellite Observations ◽

Source Database ◽

Nighttime Lights

Abstract. Emissions of CO2 from fossil fuel combustion are a critical quantity that must be accurately given in established flux inversion frameworks. Work with emerging satellite-based inversions requires spatiotemporally-detailed inventories that permit analysis of regional sources and sinks. Conventional approaches for disaggregating national emissions beyond the country and city levels based on population distribution have certain difficulties in their application. We developed a global 1 km×1 km fossil fuel CO2 emission inventory for the years 1980–2007 by combining a worldwide point source database and satellite observations of the global nightlight distribution. In addition to estimating the national emissions using global energy consumption statistics, emissions from point sources were estimated separately and were spatially allocated to exact locations indicated by the point source database. Emissions from other sources were distributed using a special nightlight dataset that had fewer saturated pixels compared with regular nightlight datasets. The resulting spatial distributions differed in several ways from those derived using conventional population-based approaches. Because of the inherent characteristics of the nightlight distribution, source regions corresponding to human settlements and land transportation were well articulated. Our distributions showed good agreement with a high-resolution inventory across the US at spatial resolutions that were adequate for regional flux inversions. The inventory will be incorporated into models for operational flux inversions that use observational data from the Japanese Greenhouse Gases Observing SATellite (GOSAT).

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Sea surface salinity subfootprint variability from a global high-resolution model

10.31223/x5kp77 ◽

2021 ◽

Author(s):

Frederick Bingham ◽

Susannah Brodnitz ◽

Severine Fournier ◽

Karly Ulfsax ◽

Akiko Hayashi ◽

...

Keyword(s):

High Resolution ◽

Satellite Observations ◽

Sea Surface ◽

Sea Surface Salinity ◽

Surface Salinity ◽

Representation Error ◽

Resolution Model ◽

One Year ◽

The One ◽

Model Year

Subfootprint variability (SFV) is variability at a spatial scale smaller than the footprint of a sat-ellite, and cannot be resolved by satellite observations. It is important to quantify and understand as it contributes to the error budget for satellite data. The purpose of this study is to estimate the SFV for sea surface salinity (SSS) satellite observations. This is done using a high-resolution (1/48°) numerical model, the MITgcm, from which one year of output has recently become availa-ble. SFV, defined as the weighted standard deviation of SSS within the satellite footprint, was computed from the model for a 2°X2° grid of points for the one model year. We present maps of SFV for 40 and 100 km footprint size, display histograms of its distribution for a range of foot-print sizes and quantify its seasonality. At 100 km (40 km) footprint size, SFV has a mode of 0.06 (0.04). It is found to vary strongly by location and season. It has larger values in western bound-ary and eastern equatorial regions, and a few other areas. SFV has strong variability throughout the year, with generally largest values in the fall season. We also quantify representation error, the degree of mismatch between random samples within a footprint and the footprint average. Our estimates of SFV and representation error can be used in understanding errors in satellite obser-vation of SSS.

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