Marco L. Carrera

Evaluation of Snowpack Simulations over the Canadian Rockies with an Experimental Hydrometeorological Modeling System

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’...

The Canadian Land Data Assimilation System (CaLDAS): Description and Synthetic Evaluation Study

AbstractThe Canadian Land Data Assimilation System (CaLDAS) has been developed at the Meteorological Research Division of Environment Canada (EC) to better represent the land surface initial states in environmental prediction and assimilation systems. CaLDAS is built around an external land surface modeling system and uses the ensemble Kalman filter (EnKF) methodology. A unique feature of CaLDAS is the use of improved precipitation forcing through the assimilation of precipitation observations. An ensemble of precipitation analyses is generated by combining numerical weather prediction (NWP) model precipitation forecasts with precipitation observations. Spatial phasing errors to the NWP first-guess precipitation forecasts are more effective than perturbations to the precipitation observations in decreasing (increasing) the exceedance ratio (uncertainty ratio) scores and generating flatter, more reliable ranked histograms. CaLDAS has been configured to assimilate L-band microwave brightness temperature TB ...

Impact of Surface Parameter Uncertainties within the Canadian Regional Ensemble Prediction System

AbstractThe aim of this study is to assess the impact of uncertainties in surface parameter and initial conditions on numerical prediction with the Canadian Regional Ensemble Prediction System (REPS). As part of this study, the Canadian version of the Interactions between Soil–Biosphere–Atmosphere (ISBA) land surface scheme has been coupled to Environment Canada’s numerical weather prediction model within the REPS. For 20 summer periods in 2009, stochastic perturbations of surface parameters have been generated in several experiments. Each experiment corresponds to 20 simulations differing by the perturbations at the initial time of one or several surface parameters or prognostic variables. The sensitivity to these perturbations is quantified especially for 2-m temperature, 10-m wind speed, cloud fraction, and precipitation up to 48-h lead time. Spatial variability of these sensitivities over the North American continent shows that soil moisture, albedo, leaf area index, and SST have the largest impacts o...

The Pan-Canadian High Resolution (2.5 km) Deterministic Prediction System

AbstractSince November 2014, the Meteorological Services of Canada (MSC) has been running a real-time numerical weather prediction system that provides deterministic forecasts on a regional domain with a 2.5-km horizontal grid spacing covering a large portion of Canada using the Global Environmental Multiscale (GEM) forecast model. This system, referred to as the High Resolution Deterministic Prediction System (HRDPS), is currently downscaled from MSC’s operational 10-km GEM-based regional system but uses initial surface fields from a high-resolution (2.5 km) land data assimilation system coupled to the HRDPS and initial hydrometeor fields from the forecast of a 2.5-km cycle, which reduces the spinup time for clouds and precipitation. Forecast runs of 48 h are provided four times daily. The HRDPS was tested and compared to the operational 10-km system. Model runs from the two systems were evaluated against surface observations for common weather elements (temperature, humidity, winds, and precipitation), ...

Warm Season Evaluation of Soil Moisture Prediction in the Soil, Vegetation, and Snow (SVS) Scheme

AbstractA 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 highe...

The Multibudget Soil, Vegetation, and Snow (SVS) Scheme for Land Surface Parameterization: Offline Warm Season Evaluation

AbstractA 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 conduc...

Field-Scale Spatial Variability of Soil Moisture and L-Band Brightness Temperature from Land Surface Modeling

AbstractAlthough soil moisture is an essential variable within the Earth system and has been extensively investigated, there is still a limited understanding of its spatiotemporal distribution and variability. Thus, the objective of this study is to attempt to reproduce the spatial variability of soil moisture and brightness temperature as measured by point-based and airborne remote sensing measurements. To do so, Environment and Climate Change Canada’s Surface Prediction System (SPS) is run at very high resolution (100 m) over a region of Manitoba (Canada) where an extensive soil moisture experiment took place in the summer of 2012 [SMAP Validation Experiment 2012 (SMAPVEX12)]. Results show that realistic finescale soil texture improves the quality of SPS outputs. Soil moisture spatial average evolution in time is well simulated by SPS. Simulated spatial variability is underestimated when compared to point-based measurements, although results are improved when examined domainwide versus comparisons using...

Impacts of SMAP data in Environment Canada's Regional Deterministic Prediction System

SMAP brightness temperature observations are assimilated within the Canadian Land Data Assimilation System, together with screen-level observations, and the resulting soil moisture and surface temperature analyses are evaluated in the context of Environment Canadas Regional Deterministic Prediction System.

Enhancements of SMOS level 2 soil moisture products over Canada

The Soil Moisture Ocean Salinity (SMOS) mission was launched in 2009 and provides derived soil moisture globally using a forward modelling approach that incorporates a number of auxiliary data sets. By default, the SMOS mission uses global land cover and soils data sets to run the soil moisture retrieval models. This study examines the use of national data sets from Agriculture and Agri-Food Canada (AAFC) to determine if improvements in land cover and soils accuracies achieved using these national data sets can provide an improvement in SMOS soil moisture retrieval. Results show that changing the land cover produced the greatest differences, with a reduction in the fraction of the land area identified as forest, but also an increase in the number of failed model retrievals. The use of the AAFC soils resulted in a greater fraction of clay in the surface soil layer, but this did not have a large impact on the overall retrieval accuracy at the study sites. This suggests that the default SMOS parameterization can provide adequate estimation of soil moisture over most sites, but areas where forest, wetland or urban land cover may be over or underestimated should be more closely evaluated.

Assimilation of SMAP brightness temperatures in environment and climate change Canada's new land surface parameterization scheme

The NASA Soil Moisture Active Passive (SMAP) mission was launched in January 2015 and has been providing near global coverage of soil moisture every 3 days. At Environment and Climate Change Canada (ECCC) considerable effort has been focused upon the assimilation of SMAP brightness temperatures for a better analysis of the soil moisture state and resulting Numerical Weather Prediction (NWP) forecasts. A new land-surface parameterization, Soil, Vegetation, and Snow (SVS) was recently developed at ECCC which includes more sophisticated hydrology incorporating multiple soil layers where soil moisture evolves according to Darcian flow, and includes separate energy budgets for different land-surface components. The objectives of this study are to perform a set of assimilation experiments to quantify improvements in soil moisture and added NWP skill from the inclusion of SMAP data within SVS.