Anne E. Walker

Over-Lake Meteorology and Estimated Bulk Heat Exchange of Great Slave Lake in 1998 and 1999

Abstract Meteorological and thermistor moorings were deployed in Great Slave Lake during the Canadian Global Energy and Water Cycle Experiment (GEWEX) Enhanced Study (CAGES) in 1998 and 1999. Large-scale meteorology included influence from a record ENSO extending from 1997 to mid-1998. Meteorological variables varied across the lake especially during the lake-heating phase after ice breakup. Generally higher over-lake air temperature and surface water temperatures occurred in 1998, but larger vapor pressure gradients over water and ∼8% higher solar radiation was observed in 1999. Although wind speed averages were similar in both years, nearly 30% more over-lake storms with winds >10 m s−1 occurred in 1998. High sensitivity of the lake temperatures to surface wind forcing was observed in 1998 in the spring warming phase. Passive microwave imagery [from the Special Sensor Microwave Imager (SSM/I)] at 85 GHz showed a record 213 ice-free days in 1998 compared to 186 days in 1999. The extended ice-free period ...

Snow water equivalent retrieval in a Canadian boreal environment from microwave measurements using the HUT snow emission model

Snow water equivalent (SWE) is a critical parameter for climatological and hydrological studies over northern high-latitude areas. In this paper, we study the usability of the Helsinki University of Technology (HUT) snow emission model for the estimation of SWE in a Canadian boreal forest environment. The experimental data (airborne passive microwave and ground-based data) were acquired during the Boreal Ecosystem-Atmosphere Study winter field campaign held in February 1994 in Central Canada. Using the experimental dataset, surface brightness temperatures at 18 and 37 GHz (vertical polarization) were simulated with the HUT snow emission model and compared to those acquired by the airborne sensors. The results showed an important underestimation at 37 GHz (-27 K) and an overestimation at 18 GHz (10 K). In this paper, we demonstrate that the errors in the model simulations are due mainly to the extinction coefficient modeling, which is a function of snow grain size. Therefore, we propose a new semiempirical function for the extinction coefficient, based on an empirical correction to the Rayleigh scattering expression. Results presented in this paper show that the proposed function improves the HUT model accuracy to predict brightness temperature in the experimental context considered, with a mean error of /spl plusmn/5 K and /spl plusmn/9 K, respectively, at 18 and 37 GHz, and a negligible bias (less than 4 K) in both cases. These errors are comparable in magnitude to the accuracy of the radiometers used during the airborne flights. SWE was retrieved using the modified HUT snow emission model based on an iterative inversion technique. SWE was estimated with a mean error of /spl plusmn/10 mm and a negligible bias. Only a rough knowledge of mean snow grain size /spl phi/~ was required in the inversion procedure. The effects of possible errors on mean snow grain size /spl phi/~ are presented and discussed.

Canadian Experiment for Soil Moisture in 2010 (CanEx-SM10): Overview and Preliminary Results

The Canadian Experiment for Soil Moisture in 2010 (CanEx-SM10) was carried out in Saskatchewan, Canada, from 31 May to 16 June, 2010. Its main objective was to contribute to Soil Moisture and Ocean Salinity (SMOS) mission validation and the prelaunch assessment of the proposed Soil Moisture Active and Passive (SMAP) mission. During CanEx-SM10, SMOS data as well as other passive and active microwave measurements were collected by both airborne and satellite platforms. Ground-based measurements of soil (moisture, temperature, roughness, bulk density) and vegetation characteristics (leaf area index, biomass, vegetation height) were conducted close in time to the airborne and satellite acquisitions. Moreover, two ground-based in situ networks provided continuous measurements of meteorological conditions and soil moisture and soil temperature profiles. Two sites, each covering 33 km × 71 km (about two SMOS pixels) were selected in agricultural and boreal forested areas in order to provide contrasting soil and vegetation conditions. This paper describes the measurement strategy, provides an overview of the data sets, and presents preliminary results. Over the agricultural area, the airborne L-band brightness temperatures matched up well with the SMOS data (prototype 346). The radio frequency interference observed in both SMOS and the airborne L-band radiometer data exhibited spatial and temporal variability and polarization dependency. The temporal evolution of the SMOS soil moisture product (prototype 307) matched that observed with the ground data, but the absolute soil moisture estimates did not meet the accuracy requirements (0.04 m3/m3) of the SMOS mission. AMSR-E soil moisture estimates from the National Snow and Ice Data Center more closely reflected soil moisture measurements.

An Investigation of the Thermal and Energy Balance Regimes of Great Slave and Great Bear Lakes

Great Slave Lake and Great Bear Lake have large surface areas, water volumes, and high latitudinal positions; are cold and deep; and are subject to short daylight periods in winter and long ones in summer. They are dissimilar hydrologically. Great Slave Lake is part of the Mackenzie Basin flowthrough system. Great Bear Lake is hydrologically isolated in its own relatively small drainage basin and all of its inflow and outflow derive from its immediate watershed. Great Slave Lake’s outflow into the Mackenzie River is more than 8 times that from Great Bear Lake. Input from the south via the Slave River provides 82% of this outflow volume. These hydrological differences exert pronounced effects on the thermodynamics, hydrodynamics, and surface climates of each lake. The quantitative results in this study are based on limited datasets from different years that are normalized to allow comparison between the two lakes. They indicate that both lakes have regional annual air temperatures within 2°C of one another, but Great Slave Lake exhibits a much longer open-water period with higher temperatures than Great Bear Lake. During the period when the lakes are warming, each lake exerts a substantial overlake atmospheric cooling, and in the period when the lakes are cooling, each exerts a strong overlake warming. This local cooling and warming cycle is greatest over Great Bear Lake. Temperature and humidity inversions are frequent early in the lake-warming season and very strong lapse gradients occur late in the lake-cooling season. Annually, for both lakes, early ice breakup is matched with late freeze-up. Conversely, late breakup is matched with early freeze-up. The magnitudes of midlake latent heat fluxes (evaporation) and sensible heat fluxes from Great Slave Lake are substantially larger than those from Great Bear Lake during their respective open-water periods. The hypothesis that because they are both large and deep, and are located in high latitudes, Great Slave Lake and Great Bear Lake will exhibit similar surface and near-surface climates that are typical of large lakes in the high latitudes proves invalid because their different hydrological systems impose very different thermodynamic regimes on the two lakes. Additionally, such an extensive north-flowing river system as the Mackenzie is subjected to latitudinally variable meteorological regimes that will differentially influence the hydrology and energy balance of these large lakes. Great Slave Lake is very responsive to climatic variability because of the relation between lake ice and absorbed solar radiation in the high sun season and we expect that Great Bear Lake will be affected in a similar fashion.

Validation of SMOS Data Over Agricultural and Boreal Forest Areas in Canada

This study was conducted as part of the Soil Moisture and Ocean Salinity (SMOS) calibration and validation activities over agricultural and boreal forest sites located in Saskatchewan, Canada. For each site covering 33 km × 71 km (i.e., about two SMOS pixels), we examined the SMOS brightness temperature (L1c) and soil moisture (L2) products from May 1 to September 30, 2010. The consistency of these data with respect to theory and to the temporal variation of surface characteristics was first discussed at both sites. Then, the SMOS L1c (prototype 346) and L2 (prototypes 305-309) products were evaluated using the Canadian Experiment for Soil Moisture in 2010 (CanEx-SM10) ground measurements and L-band passive microwave airborne measurements, in addition to AMSR-E soil moisture estimates and simulations from the zeroth order τ- ω radiative transfer model. For both study sites, the model underestimated SMOS brightness temperatures in V polarization, whereas an overestimation was observed in H polarization. The data sets showed that both the SMOS and AMSR-E soil moisture values were underestimated compared with ground measurements collected during CanEx-SM10 but less so for the AMSR-E estimates. The SMOS soil moisture product was underestimated with a RMSE varying from 0.15 to 0.18 m3/ m3. Furthermore, the overall results showed that errors in the soil moisture estimates increased with the absolute value of soil moisture.

Combining SMMR and SSM/I Data for Time Series Analysis of Central North American Snow Water Equivalent

Abstract When Special Sensor Microwave Imager (SSM/I) and Scanning Multichannel Microwave Radiometer (SMMR) data are combined, the time series of dual-polarized, multichannel, spaceborne passive microwave brightness temperatures extends from 1978 to the present. The Meteorological Service of Canada (MSC) has developed operational snow water equivalent (SWE) retrieval algorithms for western Canada that have been applied to both SMMR and SSM/I data. Climatological research questions that demand a time series of significant length can now be addressed with passive microwave–derived datasets of this nature. Attention must be given, however, to the impact of the slightly different spatial, temporal, and radiometric characteristics between the SMMR and SSM/I data on SWE algorithm performance and, therefore, time series continuity and consistency. In this study, potential bias on SWE retrieval with the MSC algorithms caused by differences between the SMMR and SSM/I sensors is assessed with a series of comparativ...

Influence of Sensor Overpass Time on Passive Microwave-Derived Snow Cover Parameters

Abstract Passive microwave-derived retrieval of terrestrial snow water equivalent (SWE) is strongly influenced by snowpack wetness. The presence of water in the crystal matrix increases the microwave emissivity, destroying the between-channel brightness temperature gradient used to make quantitative estimates of SWE. To obtain the most accurate SWE imagery, overpass times from orbiting sensors such as the Special Sensor Microwave/Imager (SSM/I) can be chosen so the diurnally coldest and driest snowpack is being monitored. In this study we seek to evaluate the role of sensor overpass time when mapping SWE by comparing two data sets of 5-day averaged, winter season (December, January, and February) SWE imagery for a ground-validated prairie study area. The first data set is derived from SSM/I morning overpass times, the second from afternoon overpass times. Correlation analysis and modified mean bias error calculations are used to quantify the association between the two data sets. In addition, a series of principal components analysis (PCA) tests have been utilized to quantify the spatial and temporal association between the two time series. Results indicate a strong agreement in SWE retrievals between the two data sets, with little systematic bias that can be attributed to sensor overpass time. Differences in snow-covered areas are more marked, with the morning overpass data consistently estimating greater snow extent. The PCA indicates that the results of time series analysis can be influenced by the choice of sensor overpass time, although the dominant characteristics of the seasonal snow cover evolution are captured by both data sets. Surface temperature data are utilized to illustrate the dependence of algorithm performance on the physical state of the snowpack.

A Comparison of Airborne Passive Microwave Brightness Temperatures and Snowpack Properties across the Boreal Forests of Finland and Western Canada

The seasonal snowpack across the boreal forest is an important national resource in both Canada and Finland, contributing freshwater for agriculture, human consumption, and hydropower generation. In both countries, satellite passive microwave data are utilized to provide operational information on snow depth and snow water equivalent (SWE) throughout the snow cover season. Airborne passive microwave surveys conducted independently across Finland and western Canada during March and April 2005 and March 2006 provided the opportunity to assess the level of similarity in snowpack physical properties and brightness temperature response to snowpack qualities using two independent data sets. The primary objectives of these campaigns were to determine the influence of small-scale heterogeneity on satellite data, using relatively high resolution airborne measurements, and to assess the Helsinki University of Technology (HUT) snow emission model capability of predicting emitted brightness temperatures under varying snowpack and landscape conditions. Comparisons of brightness temperature emissions over different land cover types showed a clear distinction of wetlands and snow-covered ice from forested and open areas. This is reflected also as a strong relationship between 6.9-GHz measurements and fractional lake cover in both Canada and Finland, with relationships at 18 and 37 GHz being less consistent between data sets. Comparisons of experimental data versus HUT snow emission model predictions showed relatively good agreement between the simulations and airborne data, specifically for the Finnish data set.

Merging Conventional (1915–92) and Passive Microwave (1978–2002) Estimates of Snow Extent and Water Equivalent over Central North America

Abstract A detailed evaluation of snow water equivalent (SWE) and snow cover extent (SCE) derived using the combined Scanning Multichannel Microwave Radiometer (SMMR) and Special Sensor Microwave Imager (SSM/I) brightness temperature records for the 1978–2002 period was carried out for a longitudinal transect in the continental interior of North America. Comparison with in situ SWE observations showed that the SMMR brightness temperature adjustments are required to produce SWE retrievals with similar bias and rmse as observed during the SSM/I period. Underestimation of SCE in the passive microwave dataset (relative to NOAA snow charts) was identified as a systematic problem, most pronounced in early winter and during seasons with above-average snow extent. The passive microwave data were successfully merged with historical data based on strong interdataset agreement for a 1978–92 overlap period. Analysis of SWE and SCE time series for the months of December through March 1915–2002 provided information on ...

Estimating Snow Water Equivalent in Northern Regions from Satellite Passive Microwave Data

With all-weather imaging, a wide swath width, and sensitivity to volume scatter of the snowpack, satellite-derived passive microwave data are well suited to snow cover applications, though the development and validation of these techniques at high latitudes lags behind their operational use across southern Canada. This review presents key results from recent field initiatives conducted to address weaknesses in contemporary SWE retrieval algorithm performance at high latitudes. Of particular relevance is the recent progress made in (1) understanding the impact of sub-grid cell land cover variability (2) validating algorithm performance over large regions of the northern boreal forest, (3) northern boreal forest algorithm development using the improved spatial resolution and additional frequencies of data available with the 2002 launch of the Advanced Microwave Scanning Radiometer (AMSR-E), and (4) early efforts to develop a tundra specific SWE retrieval scheme. Significant advances will contribute to ongoing and future climatological, hydrological, and numerical modeling studies of high latitude energy and water cycles.