风云三号微波成像仪积雪参数反演算法初步研究

选择新疆地区作为实验区,为风云三号(FY-3)微波成像仪(MWRI)发展中国区域的积雪参数半经验反演算法。使用2003年4个月的新疆地区的台站观测资料和AMSR-E 18.7 GHz,36.5GHz和89 GHz水平和垂直极化亮温作为FY-3 MWRI的模拟数据,在Chang建立的半经验模型的基础上,采用多元线性回归分析,建立一个新算法。用已有方法去除水体、降雨、湿雪、冻土的像元后,用新算法反演了新疆地区的2004年1月的积雪参数,并分别与AMSR-E雪水当量产品和台站观测值进行比较,结果表明新算法在新疆地区优于AMSR-E的反演算法。     

Integrated assessment on multi-temporal and multi-sensor combinations for reducing cloud obscuration of MODIS snow cover products of the Pacific Northwest USA

MODIS (Moderate Resolution Imaging Spectroradiometer) snow cover products, of daily, freely available, worldwide spatial extent at medium spatial resolution, have been widely applied in regional snow cover and modeling studies, although high cloud obscuration remains a concern in some applications. In this study, various approaches including daily combination, adjacent temporal deduction, fixed-day combination, flexible multi-day combination, and multi-sensor combination are assessed to remove cloud obscuration while still maintain the temporal and spatial resolutions. The performance of the resultant snow cover maps are quantitatively evaluated against in situ observations at 244 SNOTEL stations over the Pacific Northwest USA during the period of 2006-2008 hydrological years. Results indicate that daily Terra and Aqua MODIS combination and adjacent temporal deduction can reduce cloud obscuration and classification errors although an annual mean of 37% cloud coverage remains. Classification errors in snow-covered months are actually small and tend to underestimate the snow cover. Primary errors of MODIS daily, fixed and flexible multi-day combination products occur during transient months. Flexible multi-day combination is an efficient approach to maintain the balance between temporal resolution and realistic estimation of snow cover extent since it uses two thresholds to control the combination processes. Multi-sensor combinations (daily or multi-day), taking advantage of MODIS high spatial resolution and AMSR-E cloud penetration ability, provide cloud-free products but bring larger image underestimation errors as compared with their MODIS counterparts.     

Seasonality and autocorrelation of satellite-derived soil moisture products

This study presents an analysis of temporal behaviour of in situ and satellite-derived soil moisture data. The main objective is to evaluate the temporal reliability of the satellite products, comparing them with in situ data, for applications that would benefit from the use of consistent time series of soil moisture, such as studies on climate and hydrological cycle. The time series, seasonalities, and anomalies of Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) soil moisture and European Remote Sensing (ERS) satellite soil wetness index data sets were analysed over five test sites. The agreement of temporal behaviours and autocorrelation functions and the correlation with in situ data were investigated. A good agreement between the seasonalities of both satellite data sets and in situ data with high correlations (i.e. 0.9) was found over the sites with a large soil moisture variability range and short vegetation cover. Noisier seasonalities were found over sites with small soil moisture variability ranges, affected by radiofrequency interference (RFI) and characterized by croplands. In spite of ERS soil moisture being characterized by a longer time series, the seasonality is much noisier than the AMSR-E products due to the numerous gaps in the data set. The correlation among the anomalies is lower than 0.6, mainly due to the noise in the satellite products. However, the autocorrelation functions show that the anomalies are not random, although noisy. Although the stability of the anomaly correlograms is affected by the relatively short time series available for this study, the analysis shows that there are statistical similarities between the satellite soil moisture anomalies and the in situ data anomalies. The results show that AMSR-E and ERS products are consistent over long time periods and do contain useful information about soil moisture seasonality and anomaly behaviour, although they are affected by noise.     

Toward improved daily snow cover mapping with advanced combination of MODIS and AMSR-E measurements

Taking three snow seasons from November 1 to March 31 of year 2002 to 2005 in northern Xinjiang, China as an example, this study develops a new daily snow cover product (500 m) through combining MODIS daily snow cover data and AMSR-E daily snow water equivalent (SWE) data. By taking advantage of both high spatial resolution of optical data and cloud transparency of passive microwave data, the new daily snow cover product greatly complements the deficiency of MODIS product when cloud cover is present especially for snow cover product on a daily basis and effectively improves daily snow detection accuracy. In our example, the daily snow agreement of the new product with the in situ measurements at 20 stations is 75.4%, which is much higher than the 33.7% of the MODIS daily product in all weather conditions, even a little higher than the 71% of the MODIS 8-day product (cloud cover of ~ 5%). Our results also indicate that i) AMSR-E daily SWE imagery generally agrees with MOD10A1 data in detecting snow cover, with overall agreement of 93.4% and snow agreement of 96.6% in the study area; ii) AMSR-E daily SWE imagery underestimates the snow covered area (SCA) due to its coarse spatial resolution; iii) The new snow cover product can better and effectively capture daily SCA dynamics during the snow seasons, which plays a significant role in reduction, mitigation, and prevention of snow-caused disasters in pastoral areas.     

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|Δ37V|) 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 |Δ37V| was significantly related to greater (lower) ground measured SWE (r² = 0.77 with monthly averages; r² = 0.67 with pentad averages). Σ|Δ37V| was only weakly correlated with lake fraction: monthly r² 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).     

Statistical evaluation of remotely sensed snow-cover products with constraints from streamflow and SNOTEL measurements

Using streamflow and Snowpack Telemetry (SNOTEL) measurements as constraints, the evaluation of the Moderate Resolution Imaging Spectroradiometer (MODIS) daily and 8-day snow-cover products is carried out using the Upper Rio Grande River Basin as a test site. A time series of the snow areal extent (SAE) of the Upper Rio Grande Basin is retrieved from the MODIS tile h09v05 covering the time period from February 2000 to June 2004 using an automatic Geographic Information System (GIS)-based algorithm developed for this study. Statistical analysis between the streamflow at Otowi (NM) station and the SAE retrieved from the two MODIS snow-cover products shows that there is a statistically significant correlation between the streamflow and SAE for both products. This relationship can be disturbed by heavy rainstorms in the later springtime, especially in May. Correlation analyses show that the MODIS 8-day product has a better correlation (r=−0.404) with streamflow and has less percentage of spurious snowmelt events in wintertime than the MODIS daily product (r=−0.300). Intercomparison of these two products, with the SNOTEL data sets as the ground truth, shows that (1) the MODIS 8-day product has higher classification accuracy for both snow and land; (2) the omission error of misclassifying snow as land is similar for both products, both are low; (3) the MODIS 8-day product has a slightly higher commission error of misclassifying land as snow than the MODIS daily product; and (4) the MODIS daily product has higher omission errors of misclassifying both snow and land as clouds. Clouds are the major cause for reduction of the overall accuracy of the MODIS daily product. Improvement in suppressing clouds in the 8-day product is obvious from this comparison study. The sacrifice is the temporal resolution that is reduced from 1 to 8 days. The significance of the results is that the 8-day product will be more useful in evaluating the streamflow response to the snow-cover extent changes, especially from the long-term point of view considering its lower temporal resolution than the daily product. For clear days, the MODIS daily algorithm works quite well or even better than the MODIS 8-day algorithm.     

Factors affecting remotely sensed snow water equivalent uncertainty

State-of-the-art passive microwave remote sensing-based snow water equivalent (SWE) algorithms correct for factors believed to most significantly affect retrieved SWE bias and uncertainty. For example, a recently developed semi-empirical SWE retrieval algorithm accounts for systematic and random error caused by forest cover and snow morphology (crystal size — a function of location and time of year). However, we have found that climate and land surface complexities lead to significant systematic and random error uncertainties in remotely sensed SWE retrievals that are not included in current SWE estimation algorithms. Joint analysis of independent meteorological records, ground SWE measurements, remotely sensed SWE estimates, and land surface characteristics have provided a unique look at the error structure of these recently developed satellite SWE products. We considered satellite-derived SWE errors associated with the snow pack mass itself, the distance to significant open water bodies, liquid water in the snow pack and/or morphology change due to melt and refreeze, forest cover, snow class, and topographic factors such as large scale root mean square roughness and dominant aspect. Analysis of the nine-year Scanning Multichannel Microwave Radiometer (SMMR) SWE data set was undertaken for Canada where many in-situ measurements are available. It was found that for SMMR pixels with 5 or more ground stations available, the remote sensing product was generally unbiased with a seasonal maximum 20 mm average root mean square error for SWE values less than 100 mm. For snow packs above 100 mm, the SWE estimate bias was linearly related to the snow pack mass and the root mean square error increased to around 150 mm. Both the distance to open water and average monthly mean air temperature were found to significantly influence the retrieved SWE product uncertainty. Apart from maritime snow class, which had the greatest snow class affect on root mean square error and bias, all other factors showed little relation to observed uncertainties. Eliminating the drop-in-the-bucket averaged gridded remote sensing SWE data within 200 km of open water bodies, for monthly mean temperatures greater than − 2 °C, and for snow packs greater than 100 mm, has resulted in a remotely sensed SWE product that is useful for practical applications.     

Snowpack and runoff generation using AMSR-E passive microwave observations in the Upper Helmand Watershed, Afghanistan

Passive microwave estimates of snow water equivalent (SWE) were examined to determine their usefulness for evaluating water resources in the remote Upper Helmand Watershed, central Afghanistan. SWE estimates from the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) and the Special Sensor Microwave/Imager (SSM/I) passive microwave data were analyzed for six winter seasons, 2004-2009. A second, independent estimate of SWE was calculated for these same time periods using a hydrologic model of the watershed with a temperature index snow model driven using the Tropical Rainfall Measuring Mission (TRMM) gridded estimates of precipitation. The results demonstrate that passive microwave SWE values from SSM/I and AMSR-E are comparable. The AMSR-E sensor had improved performance in the early winter and late spring, which suggests that AMSR-E is better at detecting shallow snowpacks than SSM/I. The timing and magnitude of SWE values from the snow model and the passive microwave observations were sometimes similar with a correlation of 0.53 and accuracy between 55 and 62%. However, the modeled SWE was much lower than the AMSR-E SWE during two winter seasons in which TRMM data estimated lower than normal precipitation. Modeled runoff and reservoir storage predictions improved significantly when peak AMSR-E SWE values were used to update the snow model state during these periods. Rapid decreases in passive microwave SWE during precipitation events were also well aligned with flood flows that increased base flows by 170 and 940%. This finding supports previous northern latitude studies which indicate that the passive microwave signal's lack of scattering can be used to detect snow melt. The current study's extension to rain on snow events suggests an opportunity for added value for flood forecasting.     

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

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.     

Review of snow water equivalent retrieval methods using spaceborne passive microwave radiometry

ABSTRACT

The physical properties of a snowpack strongly influence the emissions from the substratum, making snow property retrievals feasible by means of the surface brightness temperature observed by passive microwave sensors. Depending on the spatial resolution observed, time series records of daily snow coverage and critical snowpack properties such as snow depth (SD) and snow water equivalent (SWE) could be helpful in applications ranging from modelling snow variations for water resources management in a catchment to global climatologic studies. However, the challenge of including spaceborne SWE products in operational hydrological and hydroclimate modelling applications is very demanding with limited uptake by these systems, mostly attributed to insufficient SWE estimation accuracy. The root causes of this challenge include the coarse spatial resolution of passive microwave (PM) observations that observe highly aggregated snowpack properties at the spaceborne scale, and inadequacies during the retrieval process caused by uncertainties with the forward emission modelling of snow and challenges to find robust parameterizations of the models. While the spatial resolution problem is largely in the realm of engineering design and constrained by physical restrictions, a better understanding of developed and adopted retrieval methodologies can provide the clarity needed to enhance our knowledge in this field. In this paper, we review snow depth and SWE retrieval methods using PM observations, taking only dry snow retrieval processes into consideration. Snow properties using PM observations can be modelled by purely empirical relations based on underlying physical processes, and SD and SWE can be estimated by regression-based approaches. Snow property retrievals have been refined gradually throughout four decades use of PM observations in tandem with better understanding of physical processes, inclusion of better snowpack parameterizations, improved uncertainty analysis frameworks, and applying better inversion algorithms. Studying available methods, we conclude that snowpack parameterization is key to accurate retrieval. By improving retrieval algorithm architectures to better capture dynamic snowpack evolution processes, SWE estimates are likely to improve. We conclude that this challenge can be addressed by coupling emission models and land surface models or integrating weather-driven snowpack evolution into emission models and performing inversion in a Bayesian framework.     

The impact of simulated soil temperatures on the estimation of snow depth over Siberia from SSM/I compared to a multi-model climatology

The snowpack is a key variable of the hydrological cycle. In recent years, numerous studies have demonstrated the importance of long-term monitoring of the Siberian snowpack on large spatial scales owing to evidence of increased river discharge, changes in snow fall amount and alterations with respect to the timing of ablation. This can currently only be accomplished using remote sensing methods. The main objective of this study is to take advantage of a new land surface forcing and simulation database in order to both improve and evaluate the snow depths retrieved using a dynamic snow depth retrieval algorithm. The dynamic algorithm attempts to account for the spatial and temporal internal properties of the snow cover. The passive microwave radiances used to derive snow depth were measured by the Special Sensor Microwave/ Imager (SSM/I) data between July 1987 and July 1995.The evaluation of remotely sensed algorithms is especially difficult over regions such as Siberia which are characterized by relatively sparse surface measurement networks. In addition, existing gridded climatological snow depth databases do not necessarily correspond to the same time period as the available satellite data. In order to evaluate the retrieval algorithm over Siberia for a recent multi-year period at a relatively large spatial scale, a land surface scheme reanalysis product from the Global Soil Wetness Project-Phase 2 (GSWP-2) is used in the current study. First, the high quality GSWP-2 input forcing data were used to drive a land surface scheme (LSS) in order to derive a climatological near-surface soil temperature. Four different snow depth retrieval methods are compared, two of which use the new soil temperature climatology as input. Second, a GSWP-2 snow water equivalent (SWE) climatology is computed from 12 state-of-the-art LSS over the same time period covered by the SSM/I data. This climatology was compared to the corresponding fields from the retrievals. This study reaffirmed the results of recent studies which showed that the inclusion of ancillary data into a satellite data-based snow retrieval algorithm, such as soil temperatures, can significantly improve the results. The current study also goes a step further and reveals the importance of including the monthly soil temperature variation into the retrieval, which improves results in terms of the spatial distribution of the snowpack. Finally, it is shown that further improved predictions of SWE are obtained when spatial and temporal variations in snow density are accounted for.     

Monitoring snowpack evolution using interferometric synthetic aperture radar on the North Slope of Alaska,USA

This research investigates the use of Interferometric Synthetic Aperture Radar (InSAR) to generate a time-series of snow water equivalent (SWE) for dry snow within the Kuparuk watershed, North Slope, Alaska, during the winter of 1993/1994. Maps depicting relative change in phase and the theoretical relative change in SWE between satellite acquisitions are created for 3-day periods at the end of March 1994 using both ascending and descending ERS-1 overpasses. The theoretical coefficient relating relative change in phase and relative change in SWE for C-band is found to be at least twice as large as what is expected when using a simple single-layer snow model for this study area and time period. Without any direct measurements of SWE on the ground, station measurements of snow depth and hourly wind are linked to each 3-day relative change in phase map. Along with a qualitative assessment, quantitative measures of the rate and magnitude of phase change around these stations are directly compared to the hourly wind data for a given 3-day period. InSAR-derived maps acquired around a measured precipitation event show a considerable relationship to the predominant direction of strong winds over each 3-day period while maps acquired around no measureable precipitation depict much less correlation between phase change and predominant direction of strong winds. Despite limited ground measurements to infer snowpack conditions, these results show continued promise for the InSAR technique to measure changes in snowpack conditions (e.g. SWE) at much higher resolutions than manual sampling methods or passive microwave remote sensing. The extension of this technique to current L-band InSAR satellite platforms is also discussed.     

Estimation of snow water equivalent over first-year sea ice using AMSR-E and surface observations

A SWE retrieval algorithm developed in-situ using passive microwave surface based radiometer data is applied to the Advanced Microwave Scanning Radiometer for Earth Observation System (AMSR-E). Snow water equivalent is predicted from two pixels located in Canadian Arctic Shelf Exchange Study (CASES) overwintering study area in Franklin Bay, N.W.T., Canada. Results show that the satellite SWE predictions are statistically valid with measured in-situ snow thickness data in both smooth and rough ice environments where predicted values range from 15 to 25 mm. Stronger correlation between measured and predicted data is found over smooth ice with R² value of 0.75 and 0.73 for both pixels respectively. Furthermore, a qualitative study of sea ice roughness using both passive and active microwave satellite data shows that the two pixels are rougher than the surrounding areas, but the SWE predictions do not seem to be affected significantly.     

积雪属性非均匀性对被动微波遥感积雪的影响

积雪属性的非均匀性在水平方向上表现为像元内积雪未完全覆盖和雪深分布的不均匀,在垂直方向上表现为积雪剖面上粒径和密度的不一致导致的积雪分层现象。这些积雪属性的非均匀性对被动微波遥感反演雪深或雪水当量带来很大的不确定性,并且给反演结果的验证带来不确定性。通过野外积雪的微波辐射特性观测、遥感积雪产品对比分析、积雪辐射传输模型模拟对这些问题进行阐述和探讨,为今后积雪微波遥感反演算法发展和结果验证提供参考。     

Evaluation of optical satellite remote sensing for rice paddy phenology in monsoon Asia using a continuous in situ dataset

In monsoon Asia, optical satellite remote sensing for rice paddy phenology suffers from atmospheric contaminations mainly due to frequent cloud cover. We evaluated the quality of satellite remote sensing of paddy phenology: (1) through continuous in situ observations of a paddy field in Japan for 1.5 years, we investigated phenological signals in the reflectance spectrum of the paddy field; (2) we tested daily satellite data taken by Terra/Aqua MODIS (MOD09 and L1B products) with regard to the agreement with the in situ data and the influence of cloud contamination. As a result, the in situ spectral characteristics evidently indicated some phenological changes in the rice paddy field, such as irrigation start, padding, heading, harvest and ploughing. The Enhanced Vegetation Index (EVI) was the best vegetation index in terms of agreement with the in situ data. More than 65% of MODIS observations were contaminated with clouds in this region. However, the combined use of Terra and Aqua decreased the rate of cloud contamination of the daily data to 43%. In conclusion, the most robust dataset for monitoring rice paddy phenology in monsoon Asia would be daily EVI derived from a combination of Terra/MODIS and Aqua/MODIS.     

Soil moisture estimation through ASCAT and AMSR-E sensors: An intercomparison and validation study across Europe

Global soil moisture products retrieved from various remote sensing sensors are becoming readily available with a nearly daily temporal resolution. Active and passive microwave sensors are generally considered as the best technologies for retrieving soil moisture from space. The Advanced Microwave Scanning Radiometer for the Earth observing system (AMSR-E) on-board the Aqua satellite and the Advanced SCATterometer (ASCAT) on-board the MetOp (Meteorological Operational) satellite are among the sensors most widely used for soil moisture retrieval in the last years. However, due to differences in the spatial resolution, observation depths and measurement uncertainties, validation of satellite data with in situ observations and/or modelled data is not straightforward. In this study, a comprehensive assessment of the reliability of soil moisture estimations from the ASCAT and AMSR-E sensors is carried out by using observed and modelled soil moisture data over 17 sites located in 4 countries across Europe (Italy, Spain, France and Luxembourg). As regards satellite data, products generated by implementing three different algorithms with AMSR-E data are considered: (i) the Land Parameter Retrieval Model, LPRM, (ii) the standard NASA (National Aeronautics and Space Administration) algorithm, and (iii) the Polarization Ratio Index, PRI. For ASCAT the Vienna University of Technology, TUWIEN, change detection algorithm is employed. An exponential filter is applied to approach root-zone soil moisture. Moreover, two different scaling strategies, based respectively on linear regression correction and Cumulative Density Function (CDF) matching, are employed to remove systematic differences between satellite and site-specific soil moisture data. Results are shown in terms of both relative soil moisture values (i.e., between 0 and 1) and anomalies from the climatological expectation.Among the three soil moisture products derived from AMSR-E sensor data, for most sites the highest correlation with observed and modelled data is found using the LPRM algorithm. Considering relative soil moisture values for an ~ 5 cm soil layer, the TUWIEN ASCAT product outperforms AMSR-E over all sites in France and central Italy while similar results are obtained in all other regions. Specifically, the average correlation coefficient with observed (modelled) data equals to 0.71 (0.74) and 0.62 (0.72) for ASCAT and AMSR-E-LPRM, respectively. Correlation values increase up to 0.81 (0.81) and 0.69 (0.77) for the two satellite products when exponential filtering and CDF matching approaches are applied. On the other hand, considering the anomalies, correlation values decrease but, more significantly, in this case ASCAT outperforms all the other products for all sites except the Spanish ones. Overall, the reliability of all the satellite soil moisture products was found to decrease with increasing vegetation density and to be in good accordance with previous studies. The results provide an overview of the ASCAT and AMSR-E reliability and robustness over different regions in Europe, thereby highlighting advantages and shortcomings for the effective use of these data sets for operational applications such as flood forecasting and numerical weather prediction.     

Mapping of snow water equivalent and snow depth in boreal and sub-arctic zones by assimilating space-borne microwave radiometer data and ground-based observations

The monitoring of snow water equivalent (SWE) and snow depth (SD) in boreal forests is investigated by applying space-borne microwave radiometer data and synoptic snow depth observations. A novel assimilation technique based on (forward) modelling of observed brightness temperatures as a function of snow pack characteristics is introduced. The assimilation technique is a Bayesian approach that weighs the space-borne data and the reference field on SD interpolated from discrete synoptic observations with their estimated statistical accuracy. The results obtained using SSM/I and AMSR-E data for northern Eurasia and Finland indicate that the employment of space-borne data using the assimilation technique improves the SD and SWE retrieval accuracy when compared with the use of values interpolated from synoptic observations. Moreover, the assimilation technique is shown to reduce systematic SWE/SD estimation errors evident in the inversion of space-borne radiometer data.     

AMSR-E积雪产品在内蒙地区的精度验证

使用地面积雪观测数据对2005年～2008年40°N～48°N、112°E～128°E区域的AMSR-E积雪产品进行了误差分析和精度验证,结果表明:2005年～2008年的AMSR-E积雪产品较好地反映了研究区域地面积雪信息的时间变化特征;AMSR-E积雪产品普遍地低估了地面积雪深度,相对而言,当地面积雪较薄时,AMSR-E可较好地反映积雪深度,当积雪较厚时,AMSR-E明显低估积雪深度;2005年～2006年、2006年～2007年以及2007年～2008年3个冬-春季时段AMSR-E和站点观测值的平均差值分别达7.38cm,6.87cm和22.07cm。     

Estimating northern hemisphere snow water equivalent for climate research through assimilation of space-borne radiometer data and ground-based measurements

The key variable describing global seasonal snow cover is snow water equivalent (SWE). However, reliable information on the hemispheric scale variability of SWE is lacking because traditional methods such as interpolation of ground-based measurements and stand-alone algorithms applied to space-borne observations are highly uncertain with respect to the spatial distribution of snow mass and its evolution. In this paper, an algorithm assimilating synoptic weather station data on snow depth with satellite passive microwave radiometer data is applied to produce a 30-year-long time-series of seasonal SWE for the northern hemisphere. This data set is validated using independent SWE reference data from Russia, the former Soviet Union, Finland and Canada. The validation of SWE time-series indicates overall strong retrieval performance with root mean square errors below 40 mm for cases when SWE < 150 mm. Retrieval uncertainty increases when SWE is above this threshold. The SWE estimates are also compared with results obtained by a typical stand-alone satellite passive microwave algorithm. This comparison demonstrates the benefits of the newly developed assimilation approach. Additionally, the trends and inter-annual variability of northern hemisphere snow mass during the era of satellite passive microwave measurements are shown.     

Advances in seasonal snow water equivalent (SWE) retrieval using in situ passive microwave measurements over first‐year sea ice

Dramatic changes have occurred in the Arctic over the past three decades in response to an accelerated warming that will have a significant impact on the world's climate. Snow accumulation (measured as snow water equivalent, SWE) over sea ice plays a key role in the changes observed due to its effect on the surface energy balance that dictates the timing of sea‐ice freeze‐up and decay. Increased awareness of the role of snow in the Arctic system has triggered numerous studies that have attempted to characterize snow accumulation from space since the early 1980s, but none has successfully quantified SWE on a seasonal basis.

This work presents the first seasonally valid SWE algorithm for first‐year sea ice based on in situ passive microwave radiometry. The in situ data were collected as a part of the Canadian Arctic Shelf Exchange Study (CASES) during the overwintering mission of the Canadian Coast Guard Ship (CCGS) Amundsen in 2003–2004. Previous work clearly demonstrated two different patterns of seasonal snow evolution, for which the algorithm presented in this paper accounts for. Our algorithm's results are valid for temperatures between ?5 and ?30°C and SWE in the range of 0–55 mm. Results show that the behaviour of the snow's thermophysical properties and brightness temperatures (T _b) is quite different in the winter cooling period compared with that in the warming period, where temperature gradient metamorphism begins at a SWE value of 33 mm. The SWE algorithm successfully models this change with a high degree of correlation.