The Canadian Land Data Assimilation System (CaLDAS): Description and Synthetic Evaluation Study - Carrera, Marco L.; Bélair, Stéphane; Bilodeau, Bernard (Journal of Hydrometeorology, 2015)
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 ...

Deep learning-based super-resolution climate simulator-emulator framework for urban heat studies - Wu, Yuankai; Teufel, Bernardo; Sushama, Laxmi; Bélair, Stéphane; Sun, Lijun (Geophysical Research Letters, 2021)
This proof-of-concept study couples machine learning and physical modeling paradigms to develop a computationally efficient simulator-emulator framework for generating super-resolution (<250 m) urban climate information, that is required by many sectors. To this end, a regional climate model/simulator is applied over the city of Montreal, for the summers of 2019 and 2020, at 2.5 km (LR) and 250 m (HR) resolutions, which are used to train and validate the proposed super-resolution deep learning (DL) model/emulator. The DL model uses an efficient sub-pixel convolution layer to generate HR information from LR data, with adversarial training applied to improve physical consistency. The DL model reduces temperature errors significantly over urbanized areas present in the LR simulation, while also demonstrating considerable skill in capturing the magnitude and location of heat stress indicators. These results portray the value of the innovative simulator-emulator framework, that can be extended to other seasons/periods, variables and regions.

Operational Implementation of the ISBA Land Surface Scheme in the Canadian Regional Weather Forecast Model. Part I: Warm Season Results - Bélair, Stéphane; Crevier, Louis-Philippe; Mailhot, Jocelyn; Bilodeau, Bernard; Delage, Yves (Journal of Hydrometeorology, 2003)
Keywords: Ouvrage_Collectif_Numéro1

Impact of Soil Moisture Data Characteristics on the Sensitivity to Crop Yields Under Drought and Excess Moisture Conditions - Champagne, Catherine; White, Jenelle; Berg, Aaron A.; Bélair, Stéphane; Carrera, Marco L. (Remote Sensing, 2019)
Soil moisture is often considered a direct way of quantifying agricultural drought since it is a measure of the availability of water to support crop growth. Measurements of soil moisture at regional scales have traditionally been sparse, but advances in land surface modelling and the development of satellite technology to indirectly measure surface soil moisture has led to the emergence of a number of national and global soil moisture data sets that can provide insight into the dynamics of agricultural drought. Droughts are often defined by normal conditions for a given time and place; as a result, data sets used to quantify drought need a representative baseline of conditions in order to accurately establish a normal. This presents a challenge when working with earth observation data sets which often have very short baselines for a single instrument. This study assessed three soil moisture data sets: a surface satellite soil moisture data set from the Soil Moisture and Ocean Salinity (SMOS) mission operating since 2010; a blended surface satellite soil moisture data set from the European Space Agency Climate Change Initiative (ESA-CCI) that has a long history and a surface and root zone soil moisture data set from the Canadian Meteorology Centre (CMC)’s Regional Deterministic Prediction System (RDPS). An iterative chi-squared statistical routine was used to evaluate each data set’s sensitivity to canola yields in Saskatchewan, Canada. The surface soil moisture from all three data sets showed a similar temporal trend related to crop yields, showing a negative impact on canola yields when soil moisture exceeded a threshold in May and June. The strength and timing of this relationship varied with the accuracy and statistical properties of the data set, with the SMOS data set showing the strongest relationship (peak X2 = 170 for Day of Year 145), followed by the ESA-CCI (peak X2 = 89 on Day of Year 129) and then the RDPS (peak X2 = 65 on Day of Year 129). Using short baseline soil moisture data sets can produce consistent results compared to using a longer data set, but the characteristics of the years used for the baseline are important. Soil moisture baselines of 18–20 years or more are needed to reliably estimate the relationship between high soil moisture and high yielding years. For the relationship between low soil moisture and low yielding years, a shorter baseline can be used, with reliable results obtained when 10–15 years of data are available, but with reasonably consistent results obtained with as few as 7 years of data. This suggests that the negative impacts of drought on agriculture may be reliably estimated with a relatively short baseline of data.

Warm Season Evaluation of Soil Moisture Prediction in the Soil, Vegetation, and Snow (SVS) Scheme - Alavi, Nasim; Bélair, Stéphane; Fortin, Vincent; Zhang, Shunli; Husain, Syed Zahid; Carrera, Marco L.; Abrahamowicz, Maria (Journal of Hydrometeorology, 2016)
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...

Assimilation of SMAP brightness temperatures in environment and climate change Canada's new land surface parameterization scheme - Carrera, Marco L.; Belair, Stephane; Bilodeau, Bernard; Abrahamowicz, Maria; Alavi, Nasim; Russell, Albert; Wang, Xihong (IGARSS 2017 - 2017 IEEE International Geoscience and Remote Sensing Symposium, 2017)

The Multibudget Soil, Vegetation, and Snow (SVS) Scheme for Land Surface Parameterization: Offline Warm Season Evaluation - Husain, Syed Zahid; Alavi, Nasim; Bélair, Stéphane; Carrera, Marco L.; Zhang, Shunli; Fortin, Vincent; Abrahamowicz, Maria; Gauthier, Nathalie (Journal of Hydrometeorology, 2016)
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...

Impacts of Adjusting Solid Precipitation Amounts in the Canadian Precipitation Analysis System - Aubry, Catherine; Bélair, Stéphane; Thériault, Julie M.; Mekis, Eva; Feng, Pei-Ning; Lespinas, Franck; Khedhaouiria, Dikra; Beaudry, Florence (Journal of Applied Meteorology and Climatology, 2025)
Abstract Real-time precipitation data are essential for weather forecasting, flood prediction, drought monitoring, irrigation, fire prevention, and hydroelectric management. To optimize these activities, reliable precipitation estimates are crucial. Environment and Climate Change Canada (ECCC) leads the Canadian Precipitation Analysis (CaPA) project, providing near-real-time precipitation estimates across North America. However, during winter, CaPA’s 6-hourly accuracy is limited because many automatic surface observations are not assimilated due to wind-induced gauge undercatch. The objective of this study is to evaluate the added value of adjusted hourly precipitation amounts for gauge undercatch due to wind speed in CaPA. A recent ECCC dataset of hourly precipitation measurements from automatic precipitation gauges across Canada is included in CaPA as part of this study. Precipitation amounts are adjusted based on several types of transfer functions, which convert measured precipitation into what high-quality equipment would have measured with reduced undercatch. First, there are no notable differences in CaPA when comparing the performance of the universal transfer function with that of several climate-specific transfer functions based on wind speed and air temperature. However, increasing solid precipitation amounts using a specific type of transfer function that depends on snowfall intensity rather than near-surface air temperature is more likely to improve CaPA’s precipitation estimates during the winter season. This improvement is more evident when the objective evaluation is performed with direct comparison with the Adjusted Daily Rainfall and Snowfall (AdjDlyRS) dataset.
Keywords: Infolettre_Ete_2025

Impact of Assimilating Radar Solid Precipitation Data in the Canadian Precipitation Analysis System - Beaudry, Florence L.; Bélair, Stéphane; Thériault, Julie M.; Khedhaouiria, Dikra; Lespinas, Franck; Michelson, Daniel; Feng, Pei-Ning; Aubry, Catherine (Journal of Hydrometeorology, 2024)
Abstract The Canadian Precipitation Analysis (CaPA) system provides near-real-time precipitation analyses over Canada by combining observations with short-term numerical weather prediction forecasts. CaPA’s snowfall estimates suffer from the lack of accurate solid precipitation measurements to correct the first-guess estimate. Weather radars have the potential to add precipitation measurements to CaPA in all seasons but are not assimilated in winter due to radar snowfall estimate imprecision and lack of precipitation gauges for calibration. The main objective of this study is to assess the impact of assimilating Canadian dual-polarized radar-based snowfall data in CaPA to improve precipitation estimates. Two sets of experiments were conducted to evaluate the impact of including radar snowfall retrievals, one set using the high-resolution CaPA (HRDPA) with the currently operational quality control configuration and another increasing the number of assimilated surface observations by relaxing quality control. Experiments spanned two winter seasons (2021 and 2022) in central Canada, covering part of the entire CaPA domain. The results showed that the assimilation of radar-based snowfall data improved CaPA’s precipitation estimates 81.75% of the time for 0.5-mm precipitation thresholds. An increase in the probability of detection together with a decrease in the false alarm ratio suggested an improvement of the precipitation spatial distribution and estimation accuracy. Additionally, the results showed improvements for both precipitation mass and frequency biases for low precipitation amounts. For larger thresholds, the frequency bias was degraded. The results also indicated that the assimilation of dual-polarization radar data is beneficial for the two CaPA configurations tested in this study.
Keywords: Infolettre; Infolettre_Septembre_2024

Comparative Analysis of High-Resolution Soil Moisture Simulations from the Soil, Vegetation, and Snow (SVS) Land Surface Model Using SAR Imagery Over Bare Soil - Dabboor, Mohammed; Sun, Lili; Sun, Leqiang; Carrera, Marco L.; Friesen, Matthew; Merzouki, Amine; McNairn, Heather; Powers, Jarrett; Bélair, Stéphane (Water, 2019)
Soil moisture is a key variable in Earth systems, controlling the exchange of water and energy between land and atmosphere. Thus, understanding its spatiotemporal distribution and variability is important. Environment and Climate Change Canada (ECCC) has developed a new land surface parameterization, named the Soil, Vegetation, and Snow (SVS) scheme. The SVS land surface scheme features sophisticated parameterizations of hydrological processes, including water transport through the soil. It has been shown to provide more accurate simulations of the temporal and spatial distribution of soil moisture compared to the current operational land surface scheme. Simulation of high resolution soil moisture at the field scale remains a challenge. In this study, we simulate soil moisture maps at a spatial resolution of 100 m using the SVS land surface scheme over an experimental site located in Manitoba, Canada. Hourly high resolution soil moisture maps were produced between May and November 2015. Simulated soil moisture values were compared with estimated soil moisture values using a hybrid retrieval algorithm developed at Agriculture and Agri-Food Canada (AAFC) for soil moisture estimation using RADARSAT-2 Synthetic Aperture Radar (SAR) imagery. Statistical analysis of the results showed an overall promising performance of the SVS land surface scheme in simulating soil moisture values at high resolution scale. Investigation of the SVS output was conducted both independently of the soil texture, and as a function of the soil texture. The SVS model tends to perform slightly better over coarser textured soils (sandy loam, fine sand) than finer textured soils (clays). Correlation values of the simulated SVS soil moisture and the retrieved SAR soil moisture lie between 0.753–0.860 over sand and 0.676-0.865 over clay, with goodness of fit values between 0.567–0.739 and 0.457–0.748, respectively. The Root Mean Square Difference (RMSD) values range between 0.058–0.062 over sand and 0.055–0.113 over clay, with a maximum absolute bias of 0.049 and 0.094 over sand and clay, respectively. The unbiased RMSD values lie between 0.038–0.057 over sand and 0.039–0.064 over clay. Furthermore, results show an Index of Agreement (IA) between the simulated and the derived soil moisture always higher than 0.90.

Spaceborne L-Band Radiometry in Environment and Climate Change Canada (ECCC)’S Numerical Analysis and Prediction Systems - Belair, Stephane; Carrera, Marco L.; Abrahamowicz, Maria; Alavi, Nasim; Badawy, Bakr; Shahabadi, Maziar Bani; Bilodeau, Bernard; Charpentier, Dorothee; Deacu, Daniel; Durnford, Dorothy; Gaborit, Etienne; Gasset, Nicolas (IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium, 2019)

Recherche en Prévision Numérique Contributions to Numerical Weather Prediction - Ritchie, H.; Bélair, Stéphane; Bernier, Natacha B.; Buehner, Mark; Charron, Martin; Fortin, Vincent; Garand, L.; Houtekamer, P.; Husain, Syed Zahid; Laroche, Stéphane; Lemieux, Jean-Franois; Lin, Hai; et al. (Atmosphere-ocean, 2022)
This is a review article invited by Atmosphere-Ocean to document the contributions of Recherche en Prévision Numérique (RPN) to Numerical Weather Prediction (NWP). It is structured as a historical review and documents RPN’s contributions to numerical methods, numerical modelling, data assimilation, and ensemble systems, with a look ahead to potential future systems. Through this review, we highlight the evolution of RPN’s contributions. We begin with early NWP efforts and continue through to environmental predictions with a broad range of applications. This synthesis is intended to be a helpful reference, consolidating developments and generating broader interest for future work on NWP in Canada.