The use of microwave radiometer data for characterizing snow storage in western China

In this study a new microwave snow retrieval algorithm was developed to account for the effects of atmospheric emission on microwave radiation over high-elevation land areas. This resulted in improved estimates of snow-covered area in western China when compared with the meteorological station data and with snow maps derived from visible imagery from the Defense Meteorological Satellite Program satellite. Some improvement in snow-depth estimation was also achieved, but a useful level of accuracy will require additional modifications tested against more extensive ground data.

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Assessment of MERRA-2 and ERA5 to Model the Snow Water Equivalent in the High Atlas (1981–2019)

Water ◽

10.3390/w13070890 ◽

2021 ◽

Vol 13 (7) ◽

pp. 890

Author(s):

Mohamed Wassim Baba ◽

Abdelghani Boudhar ◽

Simon Gascoin ◽

Lahoucine Hanich ◽

Ahmed Marchane ◽

...

Keyword(s):

River Discharge ◽

Snow Water Equivalent ◽

High Elevation ◽

Water Runoff ◽

High Atlas ◽

Seasonal Snow ◽

Snow Covered Area ◽

Covered Area ◽

Snow Water

Melt water runoff from seasonal snow in the High Atlas range is an essential water resource in Morocco. However, there are only few meteorological stations in the high elevation areas and therefore it is challenging to estimate the distribution of snow water equivalent (SWE) based only on in situ measurements. In this work we assessed the performance of ERA5 and MERRA-2 climate reanalysis to compute the spatial distribution of SWE in the High Atlas. We forced a distributed snowpack evolution model (SnowModel) with downscaled ERA5 and MERRA-2 data at 200 m spatial resolution. The model was run over the period 1981 to 2019 (37 water years). Model outputs were assessed using observations of river discharge, snow height and MODIS snow-covered area. The results show a good performance for both MERRA-2 and ERA5 in terms of reproducing the snowpack state for the majority of water years, with a lower bias using ERA5 forcing.

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Nimbus-7 SMMR Derived Global Snow Cover Parameters

Annals of Glaciology ◽

10.1017/s0260305500200736 ◽

1987 ◽

Vol 9 ◽

pp. 39-44 ◽

Cited By ~ 200

Author(s):

A.T.C. Chang ◽

J.L. Foster ◽

D.K. Hall

Keyword(s):

Snow Cover ◽

Northern Hemisphere ◽

Snow Water Equivalent ◽

Theoretical Calculations ◽

Winter Season ◽

Western China ◽

High Plains ◽

Snow Covered Area ◽

Covered Area ◽

Snow Water

Snow covers about 40 million km2of the land area of the Northern Hemisphere during the winter season. The accumulation and depletion of snow is dynamically coupled with global hydrological and climatological processes. Snow covered area and snow water equivalent are two essential measurements. Snow cover maps are produced routinely by the National Environmental Satellite Data and Information Service of the National Oceanic and Atmospheric Administration (NOAA/NESDIS) and by the US Air Force Global Weather Center (USAFGWC). The snow covered area reported by these two groups sometimes differs by several million km2, Preliminary analysis is performed to evaluate the accuracy of these products.Microwave radiation penetrating through clouds and snowpacks could provide depth and water equivalent information about snow fields. Based on theoretical calculations, snow covered area and snow water equivalent retrieval algorithms have been developed. Snow cover maps for the Northern Hemisphere have been derived from Nimbus-7 SMMR data for a period of six years (1978–1984). Intercomparisons of SMMR, NOAA/NESDIS and USAFGWC snow maps have been conducted to evaluate and assess the accuracy of SMMR derived snow maps. The total snow covered area derived from SMMR is usually about 10% less than the other two products. This is because passive microwave sensors cannot detect shallow, dry snow which is less than 5 cm in depth. The major geographic regions in which the differences among these three products are the greatest are in central Asia and western China. Future study is required to determine the absolute accuracy of each product.Preliminary snow water equivalent maps have also been produced. Comparisons are made between retrieved snow water equivalent over large area and available snow depth measurements. The results of the comparisons are good for uniform snow covered areas, such as the Canadian high plains and the Russian steppes. Heavily forested and mountainous areas tend to mask out the microwave snow signatures and thus comparisons with measured water equivalent are poorer in those areas.

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Nimbus-7 SMMR Derived Global Snow Cover Parameters

Annals of Glaciology ◽

10.3189/s0260305500200736 ◽

1987 ◽

Vol 9 ◽

pp. 39-44 ◽

Cited By ~ 85

Author(s):

A.T.C. Chang ◽

J.L. Foster ◽

D.K. Hall

Keyword(s):

Snow Cover ◽

Northern Hemisphere ◽

Snow Water Equivalent ◽

Theoretical Calculations ◽

Winter Season ◽

Western China ◽

High Plains ◽

Snow Covered Area ◽

Covered Area ◽

Snow Water

Snow covers about 40 million km2 of the land area of the Northern Hemisphere during the winter season. The accumulation and depletion of snow is dynamically coupled with global hydrological and climatological processes. Snow covered area and snow water equivalent are two essential measurements. Snow cover maps are produced routinely by the National Environmental Satellite Data and Information Service of the National Oceanic and Atmospheric Administration (NOAA/NESDIS) and by the US Air Force Global Weather Center (USAFGWC). The snow covered area reported by these two groups sometimes differs by several million km2, Preliminary analysis is performed to evaluate the accuracy of these products.Microwave radiation penetrating through clouds and snowpacks could provide depth and water equivalent information about snow fields. Based on theoretical calculations, snow covered area and snow water equivalent retrieval algorithms have been developed. Snow cover maps for the Northern Hemisphere have been derived from Nimbus-7 SMMR data for a period of six years (1978–1984). Intercomparisons of SMMR, NOAA/NESDIS and USAFGWC snow maps have been conducted to evaluate and assess the accuracy of SMMR derived snow maps. The total snow covered area derived from SMMR is usually about 10% less than the other two products. This is because passive microwave sensors cannot detect shallow, dry snow which is less than 5 cm in depth. The major geographic regions in which the differences among these three products are the greatest are in central Asia and western China. Future study is required to determine the absolute accuracy of each product.Preliminary snow water equivalent maps have also been produced. Comparisons are made between retrieved snow water equivalent over large area and available snow depth measurements. The results of the comparisons are good for uniform snow covered areas, such as the Canadian high plains and the Russian steppes. Heavily forested and mountainous areas tend to mask out the microwave snow signatures and thus comparisons with measured water equivalent are poorer in those areas.

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Snow Cover of the Upper Colorado River Basin from Satellite Passive Microwave and Visual Imagery

Hydrology Research ◽

10.2166/nh.1989.0006 ◽

1989 ◽

Vol 20 (2) ◽

pp. 73-84 ◽

Cited By ~ 8

Author(s):

Edward G. Josberger ◽

Edouard Beauvillain

Keyword(s):

Snow Cover ◽

River Basin ◽

Colorado River ◽

Microwave Radiometer ◽

Passive Microwave ◽

Colorado River Basin ◽

Upper Colorado River Basin ◽

Snow Covered Area ◽

Covered Area ◽

Snow Cover Extent

A comparison of passive microwave images from the Nimbus-7 Scanning Multichannel Microwave Radiometer (SMMR) and visual images from the Defense Meteorological Satellite Program (DMSP) of the Upper Colorado River Basin shows that passive microwave satellite imagery can be used to determine the extent of the snow cover. Eight cloud-free DMSP images throughout the winter of 1985-1986 show the extent of the snowpack, which, when compared to the corresponding SMMR images, determine the threshold microwave characteristics for snow-covered pixels. With these characteristics, the 27 sequential SMMR images give a unique view of the temporal history of the snow cover extent through the first half of the water year. Beginning mid-November, the snow-covered area rapidly increases from near zero to 80 percent by the middle of January. During late February the snow-covered area decreases as a result of basin-wide warming. The microwave determinations initially overestimate the decrease in snow cover, as a result of liquid water in the snowpack, but the return of cooler temperatures restores the veracity of the passive microwave determinations.

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Remote Sensing of Snow in High Mountain Basins in Norway

Annals of Glaciology ◽

10.1017/s0260305500010491 ◽

1985 ◽

Vol 6 ◽

pp. 250-251 ◽

Cited By ~ 1

Author(s):

T. Andersen ◽

N. Haakensen

Keyword(s):

High Mountain ◽

Snow Melt ◽

Mountain River ◽

Snow Storage ◽

Run Off ◽

Snow Covered Area ◽

Covered Area ◽

Vital Interest ◽

Snow Conditions ◽

Southern Norway

Information on snow conditions in high mountain river basins is of vital interest for flood predictions and power production. Based on techniques derived for mapping of snow cover from digital NOAA-data, relations are established between snow covered area and remaining snow storage for three basins in southern Norway. Together with estimates of the precipitation and information on maximum accumulated snow, the relation can be useful in run-off predictions for the snow-melt period.

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Should altitudinal gradients of temperature and precipitation inputs be inferred from key parameters in snow-hydrological models?

10.5194/hess-2019-556 ◽

2019 ◽

Author(s):

Denis Ruelland

Keyword(s):

Air Temperature ◽

High Elevation ◽

Hydrological Models ◽

Altitudinal Gradients ◽

Modelling Framework ◽

Temperature And Precipitation ◽

Snow Covered Area ◽

Covered Area ◽

Alpine Catchments ◽

Correct Estimation

Abstract. This paper evaluates whether snow-covered area and streamflow measurements can help assess altitudinal gradients of temperature and precipitation in data-scarce mountainous areas more realistically than using the usual interpolation procedures. An extensive dataset covering 20 Alpine catchments is used to investigate this issue. Elevation dependency in the meteorological fields is accounted for using two approaches: (i) by estimating the local and time-varying altitudinal gradients from the available gauge network based on deterministic and geostatistical interpolation methods with an external drift; and (ii) by calibrating the local gradients using an inverse snow-hydrological modelling framework. For the second approach, a simple 2-parameter model is proposed to target the temperature/precipitation-elevation relationship and to regionalise air temperature and precipitation from the sparse meteorological network. The coherence of the two approaches is evaluated by benchmarking several hydrological variables (snow-covered area, streamflow and water balance) computed with snow-hydrological models fed with the interpolated datasets and checked against available measurements. Results show that accounting for elevation dependency from scattered observations when interpolating air temperature and precipitation cannot provide sufficiently accurate inputs for models. The lack of high-elevation stations seriously limits correct estimation of lapse rates of temperature and precipitation, which, in turn, affects the performance of the snow-hydrological simulations due to imprecise estimates of temperature and precipitation volumes. Instead, retrieving the local altitudinal gradients using an inverse approach enables increased accuracy in the simulation of snow cover and discharge dynamics, while limiting problems of over-calibration and equifinality.

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Development of a Snow Depth Estimation Algorithm over China for the FY-3D/MWRI

Remote Sensing ◽

10.3390/rs11080977 ◽

2019 ◽

Vol 11 (8) ◽

pp. 977 ◽

Cited By ~ 7

Author(s):

Jianwei Yang ◽

Lingmei Jiang ◽

Shengli Wu ◽

Gongxue Wang ◽

Jian Wang ◽

...

Keyword(s):

Snow Depth ◽

Depth Estimation ◽

Estimation Algorithm ◽

Western China ◽

Retrieval Algorithm ◽

Science Data ◽

Earth Observing System ◽

Independent Evaluation ◽

Ecological Science ◽

Microwave Sensors

Launched on 15 November 2017, China’s FengYun-3D (FY-3D) has taken over prime operational weather service from the aging FengYun-3B (FY-3B). Rather than directly implementing an FY-3B operational snow depth retrieval algorithm on FY-3D, we investigated this and four other well-known snow depth algorithms with respect to regional uncertainties in China. Applicable to various passive microwave sensors, these four snow depth algorithms are the Environmental and Ecological Science Data Centre of Western China (WESTDC) algorithm, the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) algorithm, the Chang algorithm, and the Foster algorithm. Among these algorithms, validation results indicate that FY-3B and WESTDC perform better than the others. However, these two algorithms often result in considerable underestimation for deep snowpack (greater than 20 cm), while the other three persistently overestimate snow depth, probably because of their poor representation of snowpack characteristics in China. To overcome the retrieval errors that occur under deep snowpack conditions without sacrificing performance under relatively thin snowpack conditions, we developed an empirical snow depth retrieval algorithm suite for the FY-3D satellite. Independent evaluation using weather station observations in 2014 and 2015 demonstrates that the FY-3D snow depth algorithm’s root mean square error (RMSE) and bias are 6.6 cm and 0.2 cm, respectively, and it has advantages over other similar algorithms.

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Remote Sensing of Snow in High Mountain Basins in Norway

Annals of Glaciology ◽

10.3189/1985aog6-1-250-251 ◽

1985 ◽

Vol 6 ◽

pp. 250-251

Author(s):

T. Andersen ◽

N. Haakensen

Keyword(s):

High Mountain ◽

Snow Melt ◽

Mountain River ◽

Snow Storage ◽

Run Off ◽

Snow Covered Area ◽

Covered Area ◽

Vital Interest ◽

Snow Conditions ◽

Southern Norway

Information on snow conditions in high mountain river basins is of vital interest for flood predictions and power production. Based on techniques derived for mapping of snow cover from digital NOAA-data, relations are established between snow covered area and remaining snow storage for three basins in southern Norway. Together with estimates of the precipitation and information on maximum accumulated snow, the relation can be useful in run-off predictions for the snow-melt period.

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Should altitudinal gradients of temperature and precipitation inputs be inferred from key parameters in snow-hydrological models?

Hydrology and Earth System Sciences ◽

10.5194/hess-24-2609-2020 ◽

2020 ◽

Vol 24 (5) ◽

pp. 2609-2632 ◽

Cited By ~ 1

Author(s):

Denis Ruelland

Keyword(s):

Air Temperature ◽

High Elevation ◽

Hydrological Models ◽

Altitudinal Gradients ◽

Modelling Framework ◽

Temperature And Precipitation ◽

Snow Covered Area ◽

Covered Area ◽

Alpine Catchments ◽

Correct Estimation

Abstract. This paper evaluates whether snow-covered area and streamflow measurements can help assess altitudinal gradients of temperature and precipitation in data-scarce mountainous areas more efficiently than using the usual interpolation procedures. A dataset covering 20 Alpine catchments is used to investigate this issue. Elevation dependency in the meteorological fields is accounted for using two approaches: (i) by estimating the local and time-varying altitudinal gradients from the available gauge network based on deterministic and geostatistical interpolation methods with an external drift; and (ii) by calibrating the local gradients using an inverse snow-hydrological modelling framework. For the second approach, a simple two-parameter model is proposed to target the temperature/precipitation–elevation relationship and to regionalize air temperature and precipitation from the sparse meteorological network. The coherence of the two approaches is evaluated by benchmarking several hydrological variables (snow-covered area, streamflow) computed with snow-hydrological models fed with the interpolated datasets and checked against available measurements. Results show that accounting for elevation dependency from scattered observations when interpolating air temperature and precipitation cannot provide sufficiently accurate inputs for models. The lack of high-elevation stations seriously limits correct estimation of lapse rates of temperature and precipitation, which, in turn, affects the performance of the snow-hydrological simulations due to imprecise estimates of temperature and precipitation volumes. Instead, retrieving the local altitudinal gradients using an inverse approach enables increased accuracy in the simulation of snow cover and discharge dynamics while limiting problems of over-calibration and equifinality.

Download Full-text