Snow Water Equivalent Retrieval Algorithms Research Articles

Evaluating the Preconditions of Two Remote Sensing SWE Retrieval Algorithms over the US

A large amount of fresh water resources are stored in the snowpack, which is the primary source of water for streamflow in many places at middle-to-high latitude areas. Therefore, snow water equivalent (SWE) is a key parameter in the water cycle. Active and passive microwave remote sensing methods have been used to retrieve SWE due to relatively poor resolution of current in situ interpolated maps with good accuracy. However, estimation of SWE has proved challenging, despite several decades of efforts to develop retrieval approaches. Active sensors provide higher-resolution observations. Two recent promising retrieval algorithms using active data are dual frequency dual polarization backscattered power and differential interferometry. These retrieval algorithms have some restrictions on snow characteristics, the environment, and instrument properties. The restrictions limit the snow that is suitable for the specific retrieval algorithm. In order to better understand how much of the snowpack satisfies the precondition of these retrieval approaches, we use a 4 km gridded snowpack product over the contiguous US for years 1997 and 2015. We use a simple scattering model to simulate the scattering characteristics of snow. The snow property maps, simulated scattering characteristics of snow, and environmental conditions are used to filter the suitable snow for each retrieval algorithm. We show that snow wetness and vegetation coverage are the two main limiting conditions for these retrieval algorithms. We show that 39% and 44% of the grid-points with snow satisfy the preconditions of dual polarization dual frequency retrieval algorithms at 13.5 GHz (one of the recommended frequencies for this algorithm in the literature) in 1997 and 2015, respectively. The most important limiting factors for dual polarization dual frequency retrieval method are dryness of snow, penetration depth, and vegetation-free constraints. The backscattered power in dual polarization dual frequency method is more sensitive to snow density and grain radius rather than to snow depth. We also show that 55% and 53% of the grid-points with snow satisfy the precondition of differential interferometry retrieval algorithms at 1 GHz (one of the recommended frequencies for this algorithm in the literature) in 1997 and 2015, respectively. The most important precondition-limiting factors for differential interferometry are dryness of snow and vegetation-free constraints. The differential interferometry phase retrieval algorithm is equally sensitive to snow height and snow density variations and is independent of snow grain radius.

Dependence of C-Band Backscatter on Ground Temperature, Air Temperature and Snow Depth in Arctic Permafrost Regions

Microwave remote sensing has found numerous applications in areas affected by permafrost and seasonally frozen ground. In this study, we focused on data obtained by the Advanced Scatterometer (ASCAT, C-band) during winter periods when the ground is assumed to be frozen. This paper discusses the relationships of ASCAT backscatter with snow depth, air and ground temperature through correlations and the analysis of covariance (ANCOVA) to quantify influences on backscatter values during situations of frozen ground. We studied sites in Alaska, Northern Canada, Scandinavia and Siberia. Air temperature and snow depth data were obtained from 19 World Meteorological Organization (WMO) and 4 Snow Telemetry (SNOTEL) stations. Ground temperature data were obtained from 36 boreholes through the Global Terrestrial Network for Permafrost Database (GTN-P) and additional records from central Yamal. Results suggest distinct differences between sites with and without underlying continuous permafrost. Sites characterized by high freezing indices (>4000 degree-days) have consistently stronger median correlations of ASCAT backscatter with ground temperature for all measurement depths. We show that the dynamics in winter-time backscatter cannot be solely explained through snow processes, but are also highly correlated with ground temperature up to a considerable depth (60 cm). These findings have important implications for both freeze/thaw and snow water equivalent retrieval algorithms based on C-band radar measurements.

Atmospheric and Forest Decoupling of Passive Microwave Brightness Temperature Observations Over Snow-Covered Terrain in North America

This study addresses two significant sources of uncertainty prevalent in snow water equivalent (SWE) retrievals derived from advanced microwave scanning radiometer (AMSR-E) passive microwave (PMW) brightness temperature (Tb) observations at 18.7 and 36.5 GHz. Namely, atmospheric and overlying forest effects are decoupled from the original AMSR-E PMW Tb observations using relatively simple, physically based radiative transfer models. Comparisons against independent Tb measurements collected during airborne PMW Tb surveys highlight the effectiveness of the proposed AMSR-E atmospheric decoupling procedure. The atmospheric contribution to Tb ranges from 1 to 3 K depending on the frequency and polarization measured as well as meteorologic conditions at the time of AMSR-E overpass. It is further shown that forest decoupling should be conducted as a function of both land cover type and snow cover class. The exponential decay relationship between the forest structure parameter, namely, MODIS-derived leaf area index (LAI) and forest transmissivity, is fitted across snow-covered terrain in North America. The fitted exponential function can be utilized during forest decoupling activities for evergreen needle-leaved forest and woody savanna regions, but remains uncertain in other forest types due to a sparsity of snow-covered areas. By removing forest-related Tb contributions from the original AMSR-E observations, the results suggest that Tb spectral difference between 18.7 and 36.5 GHz, in general, increases across thinly vegetated to heavily vegetated regions, which can be beneficial when applied to traditional SWE retrieval algorithms.

Application of a Markov Chain Monte Carlo algorithm for snow water equivalent retrieval from passive microwave measurements

Recent applications of passive microwave remote sensing techniques to estimate snow water equivalent (SWE) increasingly rely on the comprehension of microwave emission theories, instead of traditional empirical fitting approaches. In this study, an advanced SWE retrieval algorithm based on the Markov Chain Monte Carlo method was developed. This method samples the posterior multiple-layer snow properties according to the likelihood of the brightness temperature (TB) simulation with the actual TB observations. The Microwave Emission Model of Layered Snowpacks with improved Born approximation (MEMLS-IBA) was used as the observation model. Using a globally applicable method to produce prior estimates of snow properties, the retrieval approach is called the Bayesian Algorithm for SWE Estimation with Passive Microwave measurements (BASE-PM), and was applied on 48 snowpits at Sodankylä, Finland; Churchill, Canada and Colorado, US. The result shows that the root mean squared (RMS) error of the retrieved SWE is 42.7mm excluding two outliers, and is 30.8mm if the outliers as well as six deep snowpits from Colorado are excluded. This accuracy approximately meets the 30-mm requirement of Integrated Global Observing Strategy for shallow snow. The poor performance for the outlier and deep snowpits is explained. Additional experiments using more accurate priors show that SWE retrieval accuracy can be improved with local snowcover knowledge, e.g. if historical snowpit measurements or snow process model simulations are available.

Differences Between the HUT Snow Emission Model and MEMLS and Their Effects on Brightness Temperature Simulation

Microwave emission models are a critical component of snow water equivalent retrieval algorithms applied to passive microwave measurements. Several such emission models exist, but their differences need to be systematically compared. This paper compares the basic theories of two models: the multiple-layer Helsinki University of Technology (HUT) model and the microwave emission model of layered snowpacks (MEMLS). By comparing the mathematical formulation side by side, three major differences were identified: 1) by assuming that the scattered intensity is mostly (96%) in the forward direction, the HUT model simplifies the radiative transfer equation in $4\pi$ space into two one-flux equations, whereas MEMLS uses a two-flux theory; 2) the HUT scattering coefficient is much larger than the one of MEMLS; and 3) MEMLS considers the trapped radiation inside snow due to internal reflection by a six-flux model, which is not included in HUT. Simulation experiments indicate that the large scattering coefficient of the HUT model compensates for its large forward scattering ratio to some extent, but the effects of one-flux simplification and the trapped radiation still result in different $T_{B}$ simulations between the HUT model and MEMLS. The models were compared with observations of natural snow cover at Sodankyla, Finland; Churchill, Canada; and Colorado, USA. No optimization of the snow grain size was performed. It shows that the HUT model tends to underestimate $T_{B}$ for deep snow. MEMLS with the physically based improved Born approximation performed best among the models, with a bias of −1.4 K and a root-mean-square error of 11.0 K.

Observations and Simulation of Multifrequency SAR Data Over a Snow-Covered Boreal Forest

A significant part of the Earth affected by seasonal snow is covered by forest. Moreover, the presence of forest modifies the snow accumulation and its metamorphism during the winter season. Recent studies, which were carried out within the framework of ESA’s CoReH2O Phase-A mission, demonstrate that multifrequency SAR data are able to quantify the amount of snow mass on land surfaces and or glaciers. On the other hand, the presence of forest has a significant impact on the propagation of the radar signal, depending on its structure, biomass, water content, and cover fraction. In particular, for dense forest scattering of vegetation strongly hides the signal from snow, and consequently, compromises the sensitivity to snow parameters. Within the development of the mission’s snow water equivalent (SWE) retrieval algorithm, a method to compensate the vegetation effect, and then to retrieve snow in sparse forested areas, was implemented. The method is based on the development of an e.m. model for simulating the backscattering of a snow-covered vegetated terrain and the availability of some ancillary data about forest characteristics. Model description and validation using real airborne and space-borne SAR data collected over a boreal test site in Finland are presented here. The use of the developed model in the SWE retrieval algorithm is also presented.

Automatic Grain Type Classification of Snow Micro Penetrometer Signals With Random Forests

Snow microstructure plays an important role in the remote sensing of snow water equivalent (SWE) for both passive and active microwave radars. The accuracy of microwave SWE retrieval algorithms is sensitive to (usually unknown) changes in microstructure. These algorithms could be improved with high-resolution estimates of microstructural properties by using an advanced instrument such as the Snow Micro Penetrometer (SMP), which measures penetration force at the millimeter scale and is sensitive to microstructure. The SMP can also take full micromechanical measurements at much greater speed and resolution and without observer bias than a traditional snow pit. Previous studies have shown that the snowpack stratigraphy and grain type can be accurately classified with one SMP measurement using basic statistics and classification trees (CTs). For this study, we used basic statistical measures of the penetration force and micromechanical estimates from an SMP inversion algorithm to significantly improve the classification accuracy of grain type and layer discrimination. We applied random forest (RF) techniques to classify three snow grain types (new snow, rounds, and facets) from SMP measurements collected in Switzerland and Grand Mesa, Colorado. RFs performed up to 8% better than single CTs, with overall misclassification errors between 17% and 40%. The coefficient of variation of the penetration force proved to be the most important variable, followed by variables that contain information about grain size like microscale strength and the number of ruptures.

Towards improved snow water equivalent retrieval algorithms for satellite passive microwave data over the mountainous basins of western USA

AbstractSpace‐borne passive microwave snow water equivalent (SWE) retrieval algorithms are attractive for continuous SWE monitoring over large mountainous areas. The performance of three SWE retrieval algorithms, which were considered relevant for operational purposes, was examined for each month over the Colorado River Basin. In addition, statistical post‐processing was tested as a means of improving the SWE estimates from each algorithm. The evaluation started with the so‐called Chang equation, which was a pioneer algorithm and is still used in practice. Successive attempts were then made to improve the algorithm's performance through the calibration of the equation's coefficient and through the inclusion of brightness temperature data from various frequency channels. The Chang equation consistently underestimated SWE with average bias between 30 mm in November and more than 300 mm in April and root mean square error (RMSE) exceeding 500 mm at many locations in April. The statistical post‐processing effectively removed the bias and reduced the RMSE by half for all the months. When the Chang equation's coefficients were calibrated at each site, biases were reduced by approximately 85%, and RMSE was reduced by 40%–50%. Finally, the multiple channel equations produced unbiased SWE estimates with RMSEs 50%–60% of those from the Chang equation. However, the statistical post‐processing did not reduce RMSE for both calibrated algorithms. The last algorithm produced the most reliable estimates for at‐site analysis, but its skill deteriorated when analyses were performed over larger areal extents; therefore, it is only recommended for SWE monitoring over smaller areas. For larger areas, the calibrated Chang equation is desirable because it only requires interpolations of a calibrated coefficient, which was spatially coherent. Copyright © 2011 John Wiley & Sons, Ltd.

Development of a tundra-specific snow water equivalent retrieval algorithm for satellite passive microwave data

Airborne and satellite brightness temperature ( T B) measurements were combined with intensive field observations of sub-Arctic tundra snow cover to develop the framework for a new tundra-specific passive microwave snow water equivalent (SWE) retrieval algorithm. The dense snowpack and high sub-grid lake fraction across the tundra mean that conventional brightness temperature difference approaches (such as the commonly used 37 GHz–19 GHz) are not appropriate across the sub-Arctic. Airborne radiometer measurements (with footprint dimensions of approximately 70 × 120 m) acquired across sub-Arctic Canada during three field campaigns during the 2008 winter season were utilized to illustrate a slope reversal in the 37 GHz T B versus SWE relationship. Scattering by the tundra snowpack drives a negative relationship until a threshold SWE value is reached near 130 mm at which point emission from the snowpack creates a positive but noisier relationship between 37 GHz T B and SWE. The change from snowpack scattering to emission was also evident in the temporal evolution of 37 GHz T B observed from satellite measurements. AMSR-E brightness temperatures (2002/03–2006/07) consistently exhibited decreases through the winter before reaching a minimum in February or March, followed by an increase for weeks or months before melt. The cumulative absolute change (Σ|Δ37 V|) in vertically polarized 37 GHz T B was computed at both monthly and pentad intervals from a January 1 start date and compared to ground measured SWE from intensive and regional snow survey campaigns, and climate station observations. A greater (lower) cumulative change in |Δ37 V| was significantly related to greater (lower) ground measured SWE ( r 2 = 0.77 with monthly averages; r 2 = 0.67 with pentad averages). Σ|Δ37 V| was only weakly correlated with lake fraction: monthly r 2 values calculated for January through April 2003–2007 were largely less than 0.2. These results indicate that this is a computationally straightforward and viable algorithmic framework for producing tundra-specific SWE datasets from the complete satellite passive microwave record (1979 to present).

Comparison between a multi-scattering and multi-layer snow scattering model and its parameterized snow backscattering model

Snow Water Equivalent (SWE) is a crucial parameter in the study of climatology and hydrology. Active microwave remote sensing is one of the most promising techniques for estimating the distribution of SWE at high spatial resolutions in large areas. Development of reliable and accurate inversion techniques to recover SWE is one of the most important tasks in current microwave researches. However, a number of snow pack properties, including snow density, particle size, crystal shape, stratification, ground surface roughness and soil moisture, affect the microwave scattering signals and need to be properly modeled and exploited. In this paper, we developed a multi-layer, multi-scattering model for dry snow based on recent theoretical advances in snow and surface modeling. In the proposed multi-layer model, Matrix Doubling method is used to account for scattering from each snow layer; and Advanced Integral Equation Model (AIEM) is incorporated into the model to describe surface scattering. Comparisons were made between the model predictions and field observations from NASA Cold Land Processes Field Experiment (CLPX) during Third Intensive Observation Period (IOP3) and SARALPS-2007 field experiment supported by ESA. The results indicated that model predictions were in good agreement with field observations. With the confirmed confidence, the analyses on multiple scattering, scatterer shape, and snow stratification effects were further made based on the model simulations. Furthermore, a parameterized snow backscattering model with a simple form and high computational efficiency was developed using a database generated by the multiple-scattering model. For a wide range of snow and soil properties, this parameterized model agrees well with the multiple-scattering model, with the root mean square error 0.20 dB, 0.24 dB and 0.43 dB for VV, HH and VH polarizations, respectively. This simplified model can be useful for the development of SWE retrieval algorithm and for fast simulations of radar signals over snow cover in land data assimilation systems.

Testing snow water equivalent retrieval algorithms for passive microwave remote sensing in an alpine watershed of western Canada

Brightness temperatures (TBs) from the special sensor microwave imager (SSM/I) and advanced microwave scanning radiometer (AMSR-E) from 2003 to 2007 are utilized to retrieve and evaluate the snow water equivalent (SWE) over the complex terrain of the Quesnel River Basin (QRB), British Columbia, Canada. Various algorithms including the Environment Canada (EC) algorithms, the spectral polarization difference (SPD) algorithm, and an artificial neural network (ANN) for both SSM/I and AMSR-E are evaluated against in situ SWE observations using several statistical metrics. The results show that the EC algorithms developed specifically for the southern prairies and boreal forest perform poorly across the complex topography and generally deep snow of the QRB. For other frequency combinations of SSM/I and AMSR-E measurements, significant relationships between TB difference and in situ SWE exist only when the snow accumulation is less than a threshold of 250 or 400 mm, which varies at the different in situ stations. Overall, AMSR-E provides better estimates of retrieved SWE than SSM/I. Compared to the algorithms based on TB difference, the ANNs for SSM/I and AMSR-E perform much better. The ANNs trained with all channels of AMSR-E have the best performance in fitting SWE and are able to resolve the temporal variations of SWE at all in situ stations. However, due to the complexity of the topography and vegetation in this mountainous watershed, the ANNs based only on limited in situ stations are not able to retrieve the spatial variations of SWE in this area.

Yukon River streamflow response to seasonal snow cover changes

AbstractWe used remotely sensed weekly snow water equivalent (SWE) and snow cover extent (SCE) data to investigate streamflow response to seasonal snowcover change over the Yukon watershed. We quantified the seasonal cycles and variations of snowcover (both SWE and SCE) and river streamflow, and identified a clear correspondence of river discharge to seasonal snowcover change. We also examined and compared the weekly mean streamflow with the weekly basin SWE and SCE. The results revealed a strong relation between the streamflow and snowcover change during the spring melt season. This relationship provides a practical procedure of using remotely sensed snowcover information for snowmelt runoff estimation over the large northern watersheds. Analyses of extreme (high/low) streamflow cases (years) and basin snowcover conditions indicate an association of high (low) flood peak with high (low) maximum SWE. Comparative analyses of weekly basin SWE versus SCE, peak snowmelt floods, and climatic variables (temperature and winter precipitation) indicate consistency among basin SWE, SCE, and temperature, but there is some incompatibility between basin SWE and winter precipitation. The inconsistency suggests uncertainties in determination of basin winter snowfall amounts and limitations in applications of the SWE retrieval algorithm over large watersheds/regions with different physical characteristics. Overall, the results of this study demonstrate that the weekly SWE and SCE data/products derived from remote sensing technology are useful in understanding seasonal streamflow changes in the northern regions. Copyright © 2008 John Wiley & Sons, Ltd.

Identification of atmospheric influences on the estimation of snow water equivalent from AMSR-E measurements

Radiometric measurements near 19 and 37 GHz have been used for estimation of snow water equivalent (SWE) for many years. Most conventional SWE retrieval algorithms depend on the difference between the brightness temperatures ( T b's) at these frequencies. The effect of atmospheric absorption is generally assumed to be insignificant, and thus often not taken into account in such estimation. In this paper this effect is closely examined with the aid of AMSR-E and radiosonde data sets over two widely separated regions in the continental U.S.A. Results of the analysis show that even under a clear sky the atmospheric absorption could account for as much as ∼ 25–50% to the estimation of SWE. For example, the AMSR-E estimated SWE of 10 cm would become about 13.6 cm when measured at the ground level under the same atmospheric condition; the estimation based on surface emission alone (i.e., no atmosphere) would be about 15.2 cm. There is some regional dependence of this atmospheric absorption effect, but the effect of seasonal variation is negligibly small. Under cloudy conditions, the impact of liquid cloud absorption is significant and it appears necessary to perform either cloud screening or quantify the cloud effects on SWE estimation from 19 to 37 GHz radiometric measurements at ground level or satellite altitudes.

Streamflow response to seasonal snow cover mass changes over large Siberian watersheds

We used remotely sensed weekly snow water equivalent (SWE) data (1988–2000) to investigate streamflow response to seasonal snow cover change in the large Siberian watersheds (the Ob, Yenisei, and Lena basins). We quantified the seasonal cycles and variations of snow cover mass and river streamflow and identified a clear correspondence of river discharge to seasonal snow cover mass change. We also examined and compared the weekly mean streamflow with the weekly basin SWE for the study period. The results revealed a strong relation between the streamflow and snow cover mass change during the spring melt season over the large Siberian watersheds. This relationship provides a practical procedure of using remotely sensed snow cover information for snowmelt runoff estimation over the large northern watersheds. Analyses of extreme (high/low) SWE cases (years) and the associated streamflow conditions indicate an association of high (low) flood peak with high (low) maximum SWE in the Ob and Yenisei basins. Comparative analyses of weekly basin SWE data versus snow cover extent (SCE), peak snowmelt floods, and climatic variables (temperature and winter precipitation) indicate consistency among basin SWE, SCE, and temperature but incompatibility between basin SWE and winter precipitation, particularly for the Lena watershed. The inconsistency suggests uncertainties in determination of basin winter snowfall amounts and limitations in applications of the SWE retrieval algorithm over large watersheds/regions with very different physical characteristics. Overall, the results of this study clearly demonstrate that the weekly SWE data/products derived from microwave remote sensing technology are useful in understanding seasonal streamflow changes in the arctic regions.

Evaluation of passive microwave snow water equivalent retrievals across the boreal forest/tundra transition of western Canada

Composite patterns of monthly averaged (1978–2002) satellite passive microwave derived snow water equivalent (SWE) data produced using the Meteorological Service of Canada land cover sensitive algorithm suite contain an interannually consistent and well-defined zone of high SWE retrievals (> 100 mm) across the northern boreal forest of western Canada. Consistently lower SWE retrievals are present across both the comparatively dense boreal forest to the south, and open tundra to the north. Because of the sparse conventional observing network across this region, a dedicated field sampling campaign was conducted during the 2003/2004 winter season to measure snow properties across these landscape zones in northern Manitoba, Canada. Using road and helicopter access, snow cover measurements were made along an approximately 500 km transect between the communities of Thompson (boreal forest), Gillam (sparse northern boreal forest), and Churchill (open tundra) during late November 2003, and early March 2004. The November 2003 snow survey revealed good agreement between Special Sensor Microwave/Imager (SSM/I) derived SWE retrievals and in situ measurements across the gradient of increasing SWE between Thompson and Gillam, and the well defined zone of high SWE values to the north and east of Gillam. Further to the north, SSM/I derived SWE retrievals over the boreal ecotone and open tundra were consistently lower than the in situ measurements. Results of the March survey (at which point mean snow depths had increased from approximately 30–60 cm) showed an increase in absolute SWE differences between the in situ measurements and passive microwave estimates, but relative agreement between the datasets remained strong. The same SWE gradient across the boreal forest was again captured by both datasets, although tundra retrievals remained consistently low. These findings, along with consideration of snowpack stratigraphy, density, and the potential influence of tundra lakes and ponds on microwave emission and scatter, suggest that development of a tundra-specific SWE retrieval algorithm is necessary. From a scaling perspective, a large range of SWE values were measured on the ground within each satellite footprint, but a comparison of frequency histograms showed that a high percentage of satellite retrievals (November: 93%; March: 63%) fell within ± 20 mm of the median ground measurement.

Nimbus-7 SMMR Derived Global Snow Cover Parameters

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.

Nimbus-7 SMMR Derived Global Snow Cover Parameters

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.

Nimbus-7 SMMR Derived Global Snow Cover Parameters

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.