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Marco L. Carrera
,
Stéphane Bélair
,
Vincent Fortin
,
Bernard Bilodeau
,
Dorothée Charpentier
, and
Isabelle Doré

Abstract

To improve the representation of the land surface in their operational numerical weather prediction (NWP) models, the Meteorological Research Division of Environment Canada (EC) is developing an external hydrometeorological modeling and data assimilation system. The objective of this study is to verify the improvement in simulating snow cover extent (SCE) and snow water equivalent (SWE) over the Canadian Rockies with this new modeling system. This study will be an important first step in determining the optimal configuration of the land surface model and atmospheric forcing for a future operational implementation. Simulated SCE is compared with the Interactive Multisensor Snow and Ice Mapping System (IMS) analysis, while simulated SWE values are verified against a series of manual snow survey sites located within the Canadian Rockies. Results show that land surface model simulations of SCE and SWE were sensitive to precipitation forcing. Simulations at both low and high resolution forced with EC’s experimental precipitation analysis were found to underestimate SCE and SWE values. Mountain snowpack retreated too early during the spring melt period. Precipitation forcing derived from EC’s short-range NWP model resulted in improved values for both SCE and SWE, which also contributed to higher contributions to streamflow. Terrain adjusting the atmospheric forcing data was found to be important for properly modeling local extreme SWE values. A comparison with available precipitation observations over the Canadian Rockies region found EC’s experimental precipitation analysis to possess a negative precipitation bias that increases with increasing elevation.

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Kingtse C. Mo
,
Muthuvel Chelliah
,
Marco L. Carrera
,
R. Wayne Higgins
, and
Wesley Ebisuzaki

Abstract

The large-scale atmospheric hydrologic cycle over the United States and Mexico derived from the 23-yr NCEP regional reanalysis (RR) was evaluated by comparing the RR products with satellite estimates, independent sounding data, and the operational Eta Model three-dimensional variational data assimilation (3DVAR) system (EDAS).

In general, the winter atmospheric transport and precipitation are realistic. The climatology and interannual variability of the Pacific, subtropical jet streams, and low-tropospheric moisture transport are well captured. During the summer season, the basic features and the evolution of the North American monsoon (NAM) revealed by the RR compare favorably with observations. The RR also captures the out-of-phase relationship of precipitation as well as the moisture flux convergence between the central United States and the Southwest. The RR is able to capture the zonal easterly Caribbean low-level jet (CALLJ) and the meridional southerly Great Plains low-level jet (GPLLJ). Together, they transport copious moisture from the Caribbean to the Gulf of Mexico and from the Gulf of Mexico to the Great Plains, respectively. The RR systematically overestimates the meridional southerly Gulf of California low-level jet (GCLLJ). A comparison with observations suggests that the meridional winds from the RR are too strong, with the largest differences centered over the northern Gulf of California. The strongest winds over the Gulf in the RR extend above 700 hPa, while the operational EDAS and station soundings indicate that the GCLLJ is confined to the boundary layer.

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Maziar Bani Shahabadi
,
Stéphane Bélair
,
Bernard Bilodeau
,
Marco L. Carrera
, and
Louis Garand

Abstract

A new ensemble-based land surface data assimilation (DA) system is coupled with the atmospheric four-dimensional ensemble-variational data assimilation (4D-EnVar) system with the goal of improving the analyses within Environment and Climate Change Canada’s Global Deterministic Prediction System. Since 2001, the sequential assimilation of surface variables is used to generate the initial conditions to launch the Global Environmental Multiscale (GEM) coupled forecast model. The work presented here is to replace the sequential DA with an independent surface DA system, the Canadian Land Data Assimilation System (CaLDAS) assimilating screen-level observations, and to compare assimilation experiments with CaLDAS run in uncoupled and weakly coupled modes. In the uncoupled mode, CaLDAS is used to initialize the forecast without interacting with the 4D-EnVar system. In the coupled mode, the analyses generated from CaLDAS and 4D-EnVar are used to initialize the forecast model. The analyses and forecasts from uncoupled and coupled runs are evaluated against surface and radiosonde observations over different subdomains to conclude the impact of coupling CaLDAS with 4D-EnVar. Results indicate a statistically significant reduction in bias and standard deviation at the surface for screen-level temperature and dewpoint temperature on the order of 0.1 K, and in the lower troposphere between 1000 and 500 hPa on the order of 0.1 dam for geopotential height and 0.1 K for air temperature and dewpoint depression in the coupled DA runs. The positive impact persists up to 5 days over some subdomains. It is concluded that the coupled DA approach generally performs better than the uncoupled version.

Open access
Camille Garnaud
,
Stéphane Bélair
,
Marco L. Carrera
,
Chris Derksen
,
Bernard Bilodeau
,
Maria Abrahamowicz
,
Nathalie Gauthier
, and
Vincent Vionnet

Abstract

Because of its location, Canada is particularly affected by snow processes and their impact on the atmosphere and hydrosphere. Yet, snow mass observations that are ongoing, global, frequent (1–5 days), and at high enough spatial resolution (kilometer scale) for assimilation within operational prediction systems are presently not available. Recently, Environment and Climate Change Canada (ECCC) partnered with the Canadian Space Agency (CSA) to initiate a radar-focused snow mission concept study to define spaceborne technological solutions to this observational gap. In this context, an Observing System Simulation Experiment (OSSE) was performed to determine the impact of sensor configuration, snow water equivalent (SWE) retrieval performance, and snow wet/dry state on snow analyses from the Canadian Land Data Assimilation System (CaLDAS). The synthetic experiment shows that snow analyses are strongly sensitive to revisit frequency since more frequent assimilation leads to a more constrained land surface model. The greatest reduction in spatial (temporal) bias is from a 1-day revisit frequency with a 91% (93%) improvement. Temporal standard deviation of the error (STDE) is mostly reduced by a greater retrieval accuracy with a 65% improvement, while a 1-day revisit reduces the temporal STDE by 66%. The inability to detect SWE under wet snow conditions is particularly impactful during the spring meltdown, with an increase in spatial RMSE of up to 50 mm. Wet snow does not affect the domain-wide annual maximum SWE nor the timing of end-of-season snowmelt timing in this case, indicating that radar measurements, although uncertain during melting events, are very useful in adding skill to snow analyses.

Open access
Nasim Alavi
,
Stéphane Bélair
,
Vincent Fortin
,
Shunli Zhang
,
Syed Z. Husain
,
Marco L. Carrera
, and
Maria Abrahamowicz

Abstract

A new land surface scheme has been developed at Environment and Climate Change Canada (ECCC) to provide surface fluxes of momentum, heat, and moisture for the Global Environmental Multiscale (GEM) atmospheric model. In this study, the performance of the Soil, Vegetation, and Snow (SVS) scheme in estimating the surface and root-zone soil moisture is evaluated against the Interactions between Soil, Biosphere, and Atmosphere (ISBA) scheme currently used operationally at ECCC within GEM for numerical weather prediction. In addition, the sensitivity of SVS soil moisture results to soil texture and vegetation data sources (type and fractional coverage) has been explored. The performance of SVS and ISBA was assessed against a large set of in situ observations as well as the brightness temperature data from the Soil Moisture Ocean Salinity (SMOS) satellite over North America. The results indicate that SVS estimates the time evolution of soil moisture more accurately, and compared to ISBA, results in higher correlations with observations and reduced errors. The sensitivity tests carried out during this study revealed that the SVS soil moisture results are not affected significantly by the soil texture data from different sources. The vegetation data source, however, has a major impact on the soil moisture results predicted by SVS, and accurate specification of vegetation characteristics is therefore crucial for accurate soil moisture prediction.

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Syed Zahid Husain
,
Nasim Alavi
,
Stéphane Bélair
,
Marco Carrera
,
Shunli Zhang
,
Vincent Fortin
,
Maria Abrahamowicz
, and
Nathalie Gauthier

Abstract

A new land surface parameterization scheme, named the Soil, Vegetation, and Snow (SVS) scheme, was recently developed at Environment and Climate Change Canada to replace the operationally used Interactions between Soil, Biosphere, and Atmosphere (ISBA) scheme. The new scheme is designed to address a number of weaknesses and limitations of ISBA that have been identified over the last decade. Unlike ISBA, which calculates a single energy budget for the different land surface components, SVS introduces a new tiling approach that includes separate energy budgets for bare ground, vegetation, and two different snowpacks (over bare ground and low vegetation and under high vegetation). The inclusion of a photosynthesis module as an option to determine the surface stomatal resistance is another significant addition in SVS. The representation of vertical water transport through soil has also been substantially improved in SVS with the introduction of multiple soil layers. Overall, offline simulations conducted in the present study demonstrated clear improvements in warm season meteorological predictions with SVS compared to the ISBA scheme. The results also revealed considerable reduction of standard error in the SVS-predicted L-band brightness temperature. This demonstrates the scheme’s ability for better hydrological prediction and its potential for providing more accurate soil moisture analysis. The impact of the photosynthesis module within the current implementation of SVS is, however, found to be negligible on near-surface meteorological prediction and slightly negative for brightness temperature.

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John R. Gyakum
,
Marco Carrera
,
Da-Lin Zhang
,
Steve Miller
,
James Caveen
,
Robert Benoit
,
Thomas Black
,
Andrea Buzzi
,
Cliément Chouinard
,
M. Fantini
,
C. Folloni
,
Jack J. Katzfey
,
Ying-Hwa Kuo
,
François Lalaurette
,
Simon Low-Nam
,
Jocelyn Mailhot
,
P. Malguzzi
,
John L. McGregor
,
Masaomi Nakamura
,
Greg Tripoli
, and
Clive Wilson

Abstract

The authors evaluate the performance of current regional models in an intercomparison project for a case of explosive secondary marine cyclogenesis occurring during the Canadian Atlantic Storms Project and the Genesis of Atlantic Lows Experiment of 1986. Several systematic errors are found that have been identified in the refereed literature in prior years. There is a high (low) sea level pressure bias and a cold (warm) tropospheric temperature error in the oceanic (continental) regions. Though individual model participants produce central pressures of the secondary cyclone close to the observed during the final stages of its life cycle, systematically weak systems are simulated during the critical early stages of the cyclogenesis. Additionally, the simulations produce an excessively weak (strong) continental anticyclone (cyclone); implications of these errors are discussed in terms of the secondary cyclogenesis. Little relationship between strong performance in predicting the mass field and skill in predicting a measurable amount of precipitation is found. The bias scores in the precipitation study indicate a tendency for all models to overforecast precipitation. Results for the measurable threshold (0.2 mm) indicate the largest gain in precipitation scores results from increasing the horizontal resolution from 100 to 50 km, with a negligible benefit occurring as a consequence of increasing the resolution from 50 to 25 km. The importance of a horizontal resolution increase from 100 to 50 km is also generally shown for the errors in the mass field. However, little improvement in the prediction of the cyclogenesis is found by increasing the horizontal resolution from 50 to 25 km.

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