Development and validation of a new MODIS snow-cover-extent product over China

Abstract. Based on the MOD09GA/MYD09GA 500-m surface reflectance, a new MODIS snow-cover-extent (SCE) product over China has been produced by the Northwest Institute of Eco-Environment and Resources (NIEER), Chinese Academy of Sciences. The NIEER MODIS SCE product contains two preliminary clear-sky SCE datasets — Terra-MODIS and Aqua-MODIS SCE datasets, and a final daily cloud-gap-filled (CGF) SCE dataset. The formers are generated mainly through optimizing snow-cover discriminating rules over different land-cover types, and the latter is produced after a series of gap-filling processes such as aggregating the two preliminary datasets, reducing cloud gaps with adjacent information in space and time, and eliminating all gaps with auxiliary data. Validation against 362 China Meteorological Administration (CMA) stations shows during snow seasons the overall accuracies (OA) of the three datasets are all larger than 93 %, the omission errors (OE) are all constrained within 9 %, and the commission errors (CE) are all constrained within 10 %. Biases ranging from the lowest 0.98 to the medium 1.02, to the largest 1.03 demonstrate on a whole the SCEs given by the new product are neither overestimated nor underestimated significantly. Based on the same ground reference data, we found the new product’s accuracies are clearly higher than those of standard MODIS snow products, especially for Aqua-MODIS and CGF SCE. For examples, compared with the CE of 23.78 % that the standard MYD10A1 product shows, the CE of the new Aqua-MODIS SCE dataset is 6.78 %; the OA of the new CGF SCE dataset is up to 93.15 %, versus 89.54 % of the standard MOD10A1F product and 84.36 % of the standard MYD10A1F product. Besides, as expected snow discrimination in forest areas is also improved significantly. An isolated validation at four forest CMA stations demonstrates the OA has increased by 3–10 percentage points, the OE has dropped by 1–8 percentage points, and the CE has dropped by 4–21 percentage points. Therefore, our product has virtually provided more reliable snow knowledge over China, and thereby can better serve for hydrological, climatic, environmental, and other related studies there.

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The NIEER AVHRR snow cover extent product over China – a long-term daily snow record for regional climate research

Earth System Science Data ◽

10.5194/essd-13-4711-2021 ◽

2021 ◽

Vol 13 (10) ◽

pp. 4711-4726

Author(s):

Xiaohua Hao ◽

Guanghui Huang ◽

Tao Che ◽

Wenzheng Ji ◽

Xingliang Sun ◽

...

Keyword(s):

Snow Cover ◽

Regional Climate ◽

Commission Error ◽

Cloud Detection ◽

Cohen’S Kappa ◽

Cohen's Kappa ◽

Snow Cover Extent ◽

Kappa Value ◽

Academy Of Sciences

Abstract. A long-term Advanced Very High Resolution Radiometer (AVHRR) snow cover extent (SCE) product from 1981 until 2019 over China has been generated by the snow research team in the Northwest Institute of Eco-Environment and Resources (NIEER), Chinese Academy of Sciences. The NIEER AVHRR SCE product has a spatial resolution of 5 km and a daily temporal resolution, and it is a completely gap-free product, which is produced through a series of processes such as the quality control, cloud detection, snow discrimination, and gap-filling (GF). A comprehensive validation with reference to ground snow-depth measurements during snow seasons in China revealed the overall accuracy is 87.4 %, the producer's accuracy was 81.0 %, the user's accuracy was 81.3 %, and the Cohen's kappa (CK) value was 0.717. Another validation with reference to higher-resolution snow maps derived from Landsat-5 Thematic Mapper (TM) images demonstrates an overall accuracy of 87.3 %, a producer's accuracy of 86.7 %, a user's accuracy of 95.7 %, and a Cohen's kappa value of 0.695. These accuracies were significantly higher than those of currently existing AVHRR products. For example, compared with the well-known JASMES AVHRR product, the overall accuracy increased approximately 15 %, the omission error dropped from 60.8 % to 19.7 %, the commission error dropped from 31.9 % to 21.3 %, and the CK value increased by more than 114 %. The new AVHRR product is already available at https://doi.org/10.11888/Snow.tpdc.271381 (Hao et al., 2021).

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MODIS Cloud-Gap Filled Snow-Cover Products: Advantages and Uncertainties

10.5194/hess-2019-123 ◽

2019 ◽

Cited By ~ 3

Author(s):

Dorothy K. Hall ◽

George A. Riggs ◽

Nicolo E. DiGirolamo ◽

Miguel O. Román

Keyword(s):

United States ◽

Snow Cover ◽

Environmental Science ◽

Hydrological Modeling ◽

Infrared Imaging ◽

Regional Climate ◽

Science Data ◽

Snow Cover Extent ◽

Data Record ◽

Terra Modis

Abstract. MODerate resolution Imaging Spectroradiometer (MODIS) cryosphere products that have been available since the launch of the Terra MODIS in 2000 and the Aqua MODIS in 2002 include snow-cover extent (swath, daily and eight-day composites) and daily snow albedo. These products are used in hydrological modeling and studies of local and regional climate, and are increasingly being used to study regional hydrological and climatological changes over time. Reprocessing of the complete snow-cover data record, from Collection 5 (C5) to Collection 6 (C6) and Collection 6.1 (C6.1), has led to improvements in the MODIS product suite. Suomi National Polar-orbiting Partnership (S-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Collection 1 (C1) snow-cover products have been available since 2011, and are currently being reprocessed for Collection 2 (C2). To address the need for a cloud-reduced or cloud-free daily snow product for both MODIS and VIIRS, a new daily cloud-gap filled snow-cover product was developed for MODIS C6.1 and VIIRS C2 processing. MOD10A1F (Terra) and MYD10A1F (Aqua) are daily, 500-m resolution cloud-gap filled (CGF) snow-cover map products from MODIS. VNP10A1F is the 375-m resolution CGF snow map from VIIRS. The CGF maps provide daily cloud-free snow maps, along with cloud-persistence maps showing the age of the snow or non-snow observation in each pixel. Work is ongoing to evaluate and document uncertainties in the MODIS and VIIRS standard daily CGF snow-cover products. Analysis of the MOD/MYD10A1F products for study areas in the western United States shows excellent results in terms of accuracy of snow-cover mapping. When there are frequent clear-sky episodes, MODIS is able to capture enough clear views of the surface to produce accurate snow-cover information and snow maps. Even in the extensively-cloud-covered northeastern United States during winter months, snow maps from MODIS CGF products are useful, though the snow maps are likely to miss some snow, particularly during the spring snowmelt period when snow may fall and melt within a day or two, before the clouds clear from the storm that deposited the snow. Comparisons between the Terra and Aqua CGF snow maps have revealed differences that are related to cloud masking in the two algorithms. We conclude that the MODIS Terra CGF is the more accurate MODIS snow-cover product, and should therefore be the basis of an Environmental Science Data Record that will extend the CGF data record from the Terra MODIS beginning in 2000 through the VIIRS era, at least through the early 2030s.

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Contribution of late spring Eurasian snow cover extent to Canadian winter temperatures

International Journal of Climatology ◽

10.1002/joc.2426 ◽

2011 ◽

pp. n/a-n/a ◽

Cited By ~ 1

Author(s):

Amir Shabbar ◽

Hongxu Zhao

Keyword(s):

Snow Cover ◽

Late Spring ◽

Eurasian Snow Cover ◽

Eurasian Snow ◽

Snow Cover Extent ◽

Winter Temperatures

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Changes in the Snow Cover Extent in Eurasia from Satellite Data in Relation to Hemispheric and Regional Temperature Changes

Doklady Earth Sciences ◽

10.1134/s1028334x21110106 ◽

2021 ◽

Vol 501 (1) ◽

pp. 963-968

Author(s):

I. I. Mokhov ◽

M. R. Parfenova

Keyword(s):

Snow Cover ◽

Satellite Data ◽

Temperature Changes ◽

Snow Cover Extent ◽

Regional Temperature

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Climate change in the High Mountain Asia simulated with CMIP6 models

10.5194/egusphere-egu21-8365 ◽

2021 ◽

Author(s):

Mickaël Lalande ◽

Martin Ménégoz ◽

Gerhard Krinner

Keyword(s):

High Resolution ◽

Snow Cover ◽

Climate Models ◽

Global Climate Models ◽

Cold Bias ◽

High Mountain ◽

The Tibetan Plateau ◽

Near Surface ◽

Model Biases ◽

Snow Cover Extent

<p>The High Mountains of Asia (HMA) region and the Tibetan Plateau (TP), with an average altitude of 4000 m, are hosting the third largest reservoir of glaciers and snow after the two polar ice caps, and are at the origin of strong orographic precipitation. Climate studies over HMA are related to serious challenges concerning the exposure of human infrastructures to natural hazards and the water resources for agriculture, drinking water, and hydroelectricity to whom several hundred million inhabitants of the Indian subcontinent are depending. However, climate variables such as temperature, precipitation, and snow cover are poorly described by global climate models because their coarse resolution is not adapted to the rugged topography of this region. Since the first CMIP exercises, a cold model bias has been identified in this region, however, its attribution is not obvious and may be different from one model to another. Our study focuses on a multi-model comparison of the CMIP6 simulations used to investigate the climate variability in this area to answer the next questions: (1) are the biases in HMA reduced in the new generation of climate models? (2) Do the model biases impact the simulated climate trends? (3) What are the links between the model biases in temperature, precipitation, and snow cover extent? (4) Which climate trajectories can be projected in this area until 2100? An analysis of 27 models over 1979-2014 still show a cold bias in near-surface air temperature over the HMA and TP reaching an annual value of -2.0 &#176;C (&#177; 3.2 &#176;C), associated with an over-extended relative snow cover extent of 53 % (&#177; 62 %), and a relative excess of precipitation of 139 % (&#177; 38 %), knowing that the precipitation biases are uncertain because of the undercatch of solid precipitation in observations. Model biases and trends do not show any clear links, suggesting that biased models should not be excluded in trend and projections analysis, although non-linear effects related to lagged snow cover feedbacks could be expected. On average over 2081-2100 with respect to 1995-2014, for the scenarios SSP126, SSP245, SSP370, and SSP585, the 9 available models shows respectively an increase in annual temperature of 1.9 &#176;C (&#177; 0.5 &#176;C), 3.4 &#176;C (&#177; 0.7 &#176;C), 5.2 &#176;C (&#177; 1.2 &#176;C), and 6.6 &#176;C (&#177; 1.5 &#176;C); a relative decrease in the snow cover extent of 10 % (&#177; 4.1 %), 19 % (&#177; 5 %), 29 % (&#177; 8 %), and 35 % (&#177; 9 %); and an increase in total precipitation of 9 % (&#177; 5 %), 13 % (&#177; 7 %), 19 % (&#177; 11 %), and 27 % (&#177; 13 %). Further analyses will be considered to investigate potential links between the biases at the surface and those at higher tropospheric levels as well as with the topography. The models based on high resolution do not perform better than the coarse-gridded ones, suggesting that the race to high resolution should be considered as a second priority after the developments of more realistic physical parameterizations.</p>

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High Turbidity Solis Clear Sky Model: Development and Validation

Remote Sensing ◽

10.3390/rs10030435 ◽

2018 ◽

Vol 10 (3) ◽

pp. 435 ◽

Cited By ~ 13

Author(s):

Pierre Ineichen

Keyword(s):

Model Development ◽

Clear Sky ◽

High Turbidity ◽

Development And Validation

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Precipitation and snow cover in the Himalaya: from reanalysis to regional climate simulations

Hydrology and Earth System Sciences ◽

10.5194/hess-17-3921-2013 ◽

2013 ◽

Vol 17 (10) ◽

pp. 3921-3936 ◽

Cited By ~ 70

Author(s):

M. Ménégoz ◽

H. Gallée ◽

H. W. Jacobi

Keyword(s):

Snow Cover ◽

Climate Model ◽

Regional Climate ◽

Rain Gauge ◽

Snow Accumulation ◽

The Tibetan Plateau ◽

Monsoon Period ◽

Medium Range Weather Forecast ◽

Snow Cover Extent ◽

The Himalaya

Abstract. We applied a Regional Climate Model (RCM) to simulate precipitation and snow cover over the Himalaya, between March 2000 and December 2002. Due to its higher resolution, our model simulates a more realistic spatial variability of wind and precipitation than those of the reanalysis of the European Centre of Medium range Weather Forecast (ECMWF) used as lateral boundaries. In this region, we found very large discrepancies between the estimations of precipitation provided by reanalysis, rain gauges networks, satellite observations, and our RCM simulation. Our model clearly underestimates precipitation at the foothills of the Himalaya and in its eastern part. However, our simulation provides a first estimation of liquid and solid precipitation in high altitude areas, where satellite and rain gauge networks are not very reliable. During the two years of simulation, our model resembles the snow cover extent and duration quite accurately in these areas. Both snow accumulation and snow cover duration differ widely along the Himalaya: snowfall can occur during the whole year in western Himalaya, due to both summer monsoon and mid-latitude low pressure systems bringing moisture into this region. In Central Himalaya and on the Tibetan Plateau, a much more marked dry season occurs from October to March. Snow cover does not have a pronounced seasonal cycle in these regions, since it depends both on the quite variable duration of the monsoon and on the rare but possible occurrence of snowfall during the extra-monsoon period.

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