Canadian Meteorological Centre Research Articles

Assessments of multiple metocean forecasts in the North Atlantic Ocean

Accurate wind and wave forecasts are crucial for a variety of marine operations, including ship design, navigation safety, and engineering. This study evaluates the predictability of wind and wave conditions in the North Atlantic Ocean using both deterministic and ensemble forecast models. A dataset of 29 deep water buoys was used to validate deterministic and ensemble forecasts from the National Centres for Environmental Prediction (NCEP), the Canadian Meteorological Centre (CMC), the European Centre for Medium-Range Weather Forecasts (ECMWF), and the German Weather Service (DWD). A comparison of deterministic and ensemble forecast performance was conducted using eight statistical metrics focusing on the significant wave heights associated with 10-m wind speed. The results indicate that ensemble models exhibit higher correlation coefficients and lower scatter root mean square error especially beyond the 7th forecast day, outperforming deterministic models. The findings suggest that ensemble forecasts offer a more reliable estimation of forecast, enhancing predictability for marine operations.

Validation and Application of Satellite-Derived Sea Surface Temperature Gradients in the Bering Strait and Bering Sea

The Arctic is one of the most important regions in the world’s oceans for understanding the impacts of a changing climate. Yet, it is also difficult to measure because of extreme weather and ice conditions. In this work, we directly compare four datasets from the Group for High-Resolution Sea Surface Temperature (GHRSST) with a NASA Saildrone deployment along the Alaskan Coast and the Bering Sea and Bering Strait. The four datasets used are the Remote Sensing Systems Microwave Infrared Optimally Interpolated (MWIR) product, the Canadian Meteorological Center (CMC) product, the Daily Optimally Interpolated Product (DOISST), and the Operational Sea Surface Temperature and Ice Analysis (OSTIA) product. Spatial sea surface temperature (SST) gradients were derived for both the Saildrone deployment and GHRSST products, with the GHRSST products collocated with the Saildrone deployment. Overall, statistics indicate that the OSTIA product had a correlation of 0.79 and a root mean square difference of 0.11 °C/km when compared with Saildrone. CMC had the highest correlation of 0.81. Scatter plots indicate that OSTIA had the slope closest to one, thus best reproducing the magnitudes of the Saildrone gradients. Differences increased at latitudes > 65°N where sea ice would have a greater impact. A trend analysis was then performed on the gradient fields. Overall, positive trends in gradients occurred in areas along the coastal regions. A negative trend occurred at approximately 60°N. A major finding of this study is that future work needs to revolve around the impact of changing ice conditions on SST gradients. Another major finding is that a northward shift in the southern ice edge occurred after 2010 with a maxima at approximately 2019. This indicates that the shift of the southern ice edge is not gradual but has dramatically increased over the last decade. Future work needs to revolve around examining the possible causes for this northward shift.

A comparative study of satellite-based operational analyses and ship-based in-situ observations of sea surface temperatures over the eastern Canadian shelf

The satellite-based operational sea surface temperature (SST) was compared to the ship-based in-situ SSTs established by the Atlantic Zone Monitoring Program (AZMP) over the eastern Canadian shelf (ECS) for a 3-year anal-ysis period (2005–2007). Two sets of operational SST analyses were considered in this study, with one set produced by the Canadian Meteorological Centre (CMC) and the other produced by the National Centers for Environmental Predic-tion (NCEP). The comparative study indicated that there was no appreciable systematic difference between the CMC and NCEP SST analyses over the ECS. The root mean squared difference (RMSD) between the AZMP ship-based in-situ SSTs and the satellite-based STT analyses over the ECS was about 1.0°C, without any obvious seasonal or geo-graphic trend. The RMSDs were relatively larger over the outer flank of the Grand Banks than the other regions of the ECS, mainly due to dynamically complicated circulation and hydrographic conditions over this shelf break area associated with the Labrador Current.

Development of a global operational snow analysis: The US Air Force Snow and Ice Analysis

This study describes the development of a new, near real-time (NRT) global operational snow analysis, called the US Air Force Snow and Ice Analysis (USAF-SI). USAF-SI is a timely replacement for USAF 557th Weather Wing (557 WW)’s Snow Depth Analysis Model (SNODEP), which provides a six-hourly global analysis of snow at 25-km resolution and is used by numerous other 557 WW programs and external customers. USAF-SI incorporates several major enhancements over SNODEP. USAF-SI employs additional ancillary datasets and updates the algorithms related to passive microwave-based snow depth (SD) retrieval, surface observation blending, and quality control. The spatial resolution of USAF-SI estimates is also enhanced from 25-km to 10-km. The quality of USAF-SI is evaluated by comparing it against independent in-situ measurements at ~2300 sites. USAF-SI demonstrates improved performance over SNODEP, particularly during the peak snow accumulation and spring snow melt periods. In addition, USAF-SI is compared with routinely available SD analysis such as Modern-Era Retrospective Analysis for Research and Applications, version 2, European Centre for Medium-Range Weather Forecasts ERA5-Land, and Canadian Meteorological Center. Overall, the NRT USAF-SI provides comparable skills to these high-latency products, while outperforming them in some of the regional evaluations.

Deriving Snow Depth From ICESat-2 Lidar Multiple Scattering Measurements: Uncertainty Analyses

The application of diffusion theory and Monte Carlo lidar radiative transfer simulations presented in Part I of this series of study suggests that snow depth can be derived from the first-, second- and third-order moments of the lidar backscattering pathlength distribution. These methods are now applied to the satellite ICESat-2 lidar measurements over the Arctic sea ice and land surfaces of Northern Hemisphere. Over the Arctic sea ice, the ICESat-2 retrieved snow depths agree well with co-located IceBridge snow radar measured values with a root-mean-square (RMS) difference of 7.8 cm or 29.2% of the mean snow depth. The terrestrial snow depths derived from ICESat-2 show drastic spatial variation of the snowpack along ICESat-2 ground tracks over the Northern Hemisphere, which are consistent with the University of Arizona (UA) and Canadian Meteorological Centre (CMC) gridded daily snow products. The RMS difference in snow depths between ICESat-2 and UA gridded daily snow products is 14 cm, or 28% of the mean UA snow depth. To better understand these results, we also discuss the possible sources of errors in ICESat-2 derived snow depths, including surface roughness within the laser footprint, atmospheric forward scattering, solar background noise, and detector dark current. Simulation results indicate that the snow depth errors would be less than 5 cm if the standard deviation of pulse spreading due to surface roughness is within 50 cm. Our results demonstrate that the ICESat-2 lidar measurements can be used to reliably derive snow depth, which is a critical geophysical parameter for cryosphere studies including sea ice thickness estimation and also provides important constraints in the modeling of terrestrial hydrological processes.

Gold Standard in selection of rainfall forecasting models for soybean crops region

Rainfall data forecasting is essential in agricultural sciences due to impacts caused by water excess or deficit on crop growth. Our study aimed to develop a method to select rainfall forecast models using references with negligible error denoted as the gold standard. To this end, we used forecasting models from national centers such as Canadian Meteorological Center (CMC), European Center for Medium-Range Weather Forecasts (ECMWF), National Center for Environmental Prediction (NCEP), and Center for Weather Forecasting and Climate Studies (CPTEC). The study area comprised the western mesoregion of Paraná State (Brazil), and data were gathered from October to March between the soybean crop seasons of 2010/2011 and 2015/2016. Ten-day period clusters, corresponding to 240 h forecasts in the centers, were used to assess agreement with the gold standard. Our results showed that forecasting center selection must be based on rainfall value ranges and geographic locations. Selection according to the highest agreement with the gold standard was estimated at 76.9% for range 1 in CPTEC, 38.5% for range 2 and 4 in ECMWF, and 38.5% for range 3 in NCEP. In conclusion, the proposed method was efficient in selecting forecasting centers in areas of interest

Uncertainty Characterization of Ground‐Based, Satellite, and Reanalysis Snow Depth Products Using Extended Triple Collocation

AbstractThe optimal use of hemispheric‐scale snow depth (SD) products for various hydrometeorological applications requires a comprehensive assessment of their quality. Most previous validation studies of SD products adopted in situ observations as the ground truth, which may cause representativeness errors due to spatial scale mismatch between point‐based ground SD measurements and grid‐based SD products. The extended triple collocation (ETC) technique is a powerful tool to estimate the uncertainty of three independent data sets without assuming any one data source is an error‐free “truth” reference. This study first used the ETC to assess the uncertainty of three types of hemispheric‐scale SD products, including the ground‐based analysis Canadian Meteorological Centre (CMC), the satellite‐based Advanced Microwave Scanning Radiometer 2 (AMSR2), and the model‐based Global Land Data Assimilation System (GLDAS) SD products. Furthermore, the uncertainties of each SD product were analyzed using ETC metrics, that is, the correlation coefficient (R) and error standard deviations (STDs), with respect to several environmental and perturbing factors. Overall, the CMC outperforms the AMSR2 and GLDAS, with a higher R and a smaller STD. Considering multiple environmental and perturbing factors, the poorest performance of the three SD products is mainly found in densely vegetated regions, and they are strongly related to the forest cover fraction and surface roughness. Despite the above factors, the best performance for all three SD products is found over temperate climate regions. The results demonstrate the usefulness of the ETC approach to quantify the uncertainty of SD products particularly in remote regions with sparse in situ measurements.

Testing remote sensing estimates of snow water equivalent in the framework of the European Drought Observatory

We evaluated the feasibility for operational snow drought monitoring over Europe based on the near-real-time snow water equivalent (SWE) satellite product from the EUMETSAT Satellite Application Facility on Support to Operational Hydrology and Water Management (H-SAF). To do so, the consistency of this dataset with the consolidated dataset of the Canadian Meteorological Centre (CMC), as well as with the ERA5 reanalysis dataset from the European Centre for Medium-Range Weather Forecasts, was tested in terms of both spatial snow coverage and detection of anomalies from the long-term climatology. The analysis confirms a general good agreement among the three products as well as substantial differences over mountainous terrains, with the H-SAF product capturing only about 30% of the areas identified by CMC as snow-covered in those areas, while a better match between the ERA5 and the CMC spatial coverage is observed. However, significant inconsistencies in the correlation between all three SWE anomalies are observed over mountain areas. Due to the lack of a reliable reference dataset, the observed inconsistencies and the coarse spatial resolution (0.25 deg) of all three products limit the possibility for snow drought monitoring over key European regions such as the Alps.

Gold Standard Agreement Model for Precipitation Forecast in Paraná Using Bootstrap

Demand for quality weather forecasts has increased in the last decades, leading national meteorological centers to develop new forecasting models. These models have parameterizations which can produce different predictions for the same location and agrometeorological variable. In the state of Paraná - Brazil, studies on rain forecasting are important for planning the soybean crop. The objective of this study was to compare, based on a gold-standard and using bootstrapping residuals, forecasts of total rainfall by virtual stations of the following centers: Canadian Meteorological Center (CMC), European Center for Medium-Range Weather Forecasts (ECMWF), National Centers for Environmental Prediction (NCEP) and Center for Weather Forecasting and Climate Studies (CPTEC). Gold-standard measurements were obtained from Meteorological System of Paraná (SIMEPAR) meteorological stations. The studied region was the state of Paraná, in October–March of the harvest years 2011/2012–2015/2016; forecast ranges were 24 and 240 hours. Knowledge Discovery in Databases (KDD), focused on data mining techniques, was the chosen methodology. In the data preprocessing stage, spatial and temporal stratification, cleansing and grouping were performed. For the comparisons, 24 h and 240 h weather forecasts were used, being grouped in five-day and ten-day periods, respectively, and coefficients of agreement with the gold-standard measure were calculated. The choice of forecast center should consider the geographic location of a certain pluviometric station, and the temporal range of the forecast, according to its measure of agreement with the gold standard measure. Spatial variations of forecasting centers were identified within the mesoregions, which suggests the employment of different forecasting centers in a certain mesoregion.

Evaluation of AIRS and CrIS SST Measurements Relative to Three Globally Gridded SST Products Between 2013 and 2019

Globally gridded sea surface temperatures (SSTs) provide key data for long-term monitoring of the stability of satellite data. Despite apparent limitations, accurate hyperspectral data can provide useful independent information to critique the stability of global SST products on the annual-to-decadal time scale. We compared data from atmospheric infrared sounder (AIRS) on EOS Aqua and Crosstrack Interferometer Sounder (CrIS) on SNPP with the SST products from NOAA/NESDIS [real time global (RTG)], the Canadian Meteorological Centre (CMC), and the U.K. Met Office [Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA)]. For the 2013–2019 period, the overall standard deviation of the difference between AIRS and the RTG was 0.55 K, with an increasing trend over time. In contrast, the standard deviation of the difference between the AIRS, the CMC, and the OSTIA dropped steadily to below 0.4 K, a level previously seen only in SST products relative to independent buoy data. Unexplained biases between the observed and the gridded SSTs at the 100-mK level are consistent with the already existing estimates of the AIRS and CrIS absolute calibration accuracy. However, the AIRS and CrIS observations both show artifacts in all three SST products, increasing with distance from the equator, with the CMC artifacts being the smallest. Even with the CMC, a trend of 4 mK per year relative to AIRS and CrIS was observed between 2013 and 2019 for the 30S–30N oceans. Investigation of the underlying causes of the observed discrepancies requires further work.

Investigating the Response of Temperature and Salinity in the Agulhas Current Region to ENSO Events

The Agulhas Current is a critical component of global ocean circulation and has been observed to respond to El Niño Southern Oscillation (ENSO) events via its temperature and salinity signatures. In this research, we use sea surface salinity (SSS) from the National Aeronautics and Space Administration’s (NASA) Soil Moisture Active Passive (SMAP) satellite, sea surface temperature (SST) observations from the Canadian Meteorological Centre (CMC), sea surface height (SSH) anomalies from altimetry, and the Oceanic Niño Index to study the SMAP satellite time period of April 2015 through March 2020 (to observe full years of study). We see warming and high salinities after El Niño, cooling and fresher surface waters after La Niña, and a stronger temperature response than that of salinity. About one year after the 2015 El Niño, there is a warming of the entire region except at the Antarctic Circumpolar Current. About two years after the event, there is an increase in salinity along the eastern coast of Africa and in the Agulhas Current region. About two years after the 2016 and 2018 La Niñas, there is a cooling south of Madagascar and in the Agulhas Current. There are no major changes in salinity seen in the Agulhas Current, but there is a highly saline mass of water west of the Indonesian Throughflow about two years after the La Niña events. Wavelet coherence analysis finds that SSS and ENSO are most strongly correlated a year after the 2015 El Niño and two years after the 2016 La Niña.

Evaluation and Application of Quantitative Precipitation Forecast Products for Mainland China Based on TIGGE Multimodel Data

AbstractWe evaluated 24-h control forecast products from The International Grand Global Ensemble center over the 10 first-class water resource regions of Mainland China in 2013–18 from the perspective of precipitation processes (continuous) and precipitation events (discrete). We evaluated the forecasts from the China Meteorological Administration (CMA), the Centro de Previsão de Tempo e Estudos Climáticos (CPTEC), the Canadian Meteorological Centre (CMC), the European Centre for Medium-Range Weather Forecasts (ECMWF), the Japan Meteorological Agency (JMA), the Korea Meteorological Administration (KMA), the United Kingdom Met Office (UKMO), and the National Centers for Environmental Prediction (NCEP). We analyzed the differences among the numerical weather prediction (NWP) models in predicting various types of precipitation events and showed the spatial variations in the quantitative precipitation forecast efficiency of the NWP models over Mainland China. Meanwhile, we also combined four hydrological models to conduct meteo-hydrological runoff forecasting in three typical basins and used the Bayesian model averaging (BMA) method to perform the ensemble forecast of different scenarios. Our results showed that the models generally underestimate and overestimate precipitation in northwestern China and southwestern China, respectively. This tendency became increasingly clear as the lead time rose. Each model has a high reliability for the forecast of no-rain and light rain in the next 10 days, whereas the NWP model only has high reliability on the next day for moderate and heavy rain events. In general, each model showed different capabilities of capturing various precipitation events. For example, the CMA and CMC forecasts had a better prediction performance for heavy rain but greater errors for other events. The CPTEC forecast performed well for long lead times for no-rain and light rain but had poor predictability for moderate and heavy rains. The KMA, UKMO, and NCEP forecasts performed better for no-rain and light rain. However, their forecasting ability was average for moderate and heavy rain. Although the JMA model performed better in terms of errors and accuracy, it seriously underestimated heavy rain events. The extreme rainstorm and flood forecast results of the coupled JMA model should be treated with caution. Overall, the ECMWF had the most robust performance. Discrepancies in the forecasting effects of various models on different precipitation events vary with the lead time and region. When coupled with hydrological models, NWP models not only control the accuracy of runoff prediction directly but also increase the difference among the prediction results of different hydrological models with the increase in NWP error significantly. Among all the single models, ECMWF, JMA, and NCEP have better effects than the other models. Moreover, the ensemble forecast based on BMA is more robust than the single model, which can improve the quality of runoff prediction in terms of accuracy and reliability.

Application of Multimodel Superensemble Technique on the TIGGE Suite of Operational Models

One widely recognized portal which provides numerical weather prediction forecasts is “The Observing System Research and Predictability Experiment” (THORPEX) Interactive Grand Global Ensemble (TIGGE), an initiative of WMO project. This data portal provides forecasts from 1 to 16 days (2 weeks in advance) for many variables such as rainfall, winds, geopotential height, temperature, and relative humidity. These weather forecasting centers have delivered near-real-time (with a delay of 48 hours) ensemble prediction system data to three TIGGE data archives since October 2006. This study is based on six years (2008–2013) of daily rainfall data by utilizing output from six centers, namely the European Centre for Medium-Range Weather Forecasts, the National Centre for Environmental Prediction, the Center for Weather Forecast and Climatic Studies, the China Meteorological Agency, the Canadian Meteorological Centre, and the United Kingdom Meteorological Office, and make consensus forecasts of up to 10 days lead time by utilizing the multimodal multilinear regression technique. The prediction is made over the Indian subcontinent, including the Indian Ocean. TRMM3B42 daily rainfall is used as the benchmark to construct the multimodel superensemble (SE) rainfall forecasts. Based on statistical ability ratings, the SE offers a better near-real-time forecast than any single model. On the one hand, the model from the European Centre for Medium-Range Weather Forecasting and the UK Met Office does this more reliably over the Indian domain. In a case of Indian monsoon onset, 05 June 2014, SE carries the lowest RMSE of 8.5 mm and highest correlation of 0.49 among six member models. Overall, the performance of SE remains better than any individual member model from day 1 to day 10.

Evaluation of the North American multi-model ensemble for monthly precipitation forecast

The North American multi-model ensemble (NMME) is a multi-model seasonal forecasting system consisting of a collection of models generated from several climate modelling centers. This research examined the monthly precipitation in North Maluku generated by five NMME models. The purpose of this research is to assess the performance of monthly precipitation prediction by using RMSE and Rank Histogram analysis. The NMME models are verified against observed precipitation. The analysis shows that they are biased and underdispersive. Among the five NMME models, the Center for Ocean-Land-Atmosphere Studies (COLA) exhibits the best predictive skill. The performances of the Canadian Meteorological Centre (CMC) are relatively worse than that of the other models. The COLA model shows relatively high skill when used to forecast May-November monthly precipitation. Meanwhile, the National Oceanic and Atmospheric Administration (NOAA)’s Geophysical Fluid Dynamics Laboratory (GFDL) model shows high skill in December-April periods. The ensemble forecast is calibrated with the BMA approach in order to obtain reliable forecasts.

AMSR-E Snow: Can Snowfall Help Improve SWE Estimates?

AbstractSnowfall and snowpack are tightly coupled within the snow water cycle and careful monitoring is crucial to better understand snow’s role in Earth’s water and energy cycles. Current and future estimates of the total amount of seasonal snow on the ground are limited by the variability in the initial snowfall and uncertainties in in situ and remote sensing observations. In this study, passive microwave remote sensing estimates of snowfall and snow water equivalent (SWE) from the Advanced Microwave Scanning Radiometer (AMSR-E) instrument are used to assess the consistency in the snow products. A snow evolution model, SnowModel, is employed to simulate snow processes that occur between the initial snowfall and subsequent SWE. AMSR-E is found to have significant discrepancies in both snowfall and SWE compared to MERRA-2 reanalysis and the Canadian Meteorological Centre (CMC) snow product. It is shown that AMSR-E snowfall is currently not a useful metric to estimate SWE without applying large corrections throughout the winter season. Regions of consistency in the AMSR-E snow products occur for reasons that pertain to underestimation in both snowfall and SWE. In addition to snow consistency, microwave brightness temperatures (TBs) are analyzed in response to the snowpack and snowfall physical properties. These experiments indicate significant sensitivity to regime-dependent scattering characteristics that must be accounted for to accurately estimate global snow properties and provide better physical consistency in the snow products from remote sensing platforms.

Evaluating the Absolute Calibration Accuracy and Stability of AIRS Using the CMC SST

We compare the daily mean and standard deviation of the difference between the sea surface skin temperature (SST) derived from clear sky Atmospheric InfraRed Sounder (AIRS) data from seven atmospheric window channels between 2002 and 2020 and collocated Canadian Meteorological Centre (CMC) SST data from the tropical oceans. After correcting the mean difference for cloud contamination and diurnal effects, the remaining bias relative to the CMC SST, is reasonably consistent with estimates of the AIRS absolute accuracy based on the uncertainty of the pre-launch calibration. The time series of the bias produces trends well below the 10 mK/yr level required for climate change evaluations. The trends are in the 2 mK/yr range for the five window channels between 790 and 1231 cm−1, and +5 mK/yr for the shortwave channels. Between 2002 and 2020, the time series of the standard deviation of the difference between the AIRS SST and the CMC SST dropped fairly steadily to below 0.4 K in several AIRS window channels, a level previously only seen in gridded SST products relative to the Argo buoys.

Inter-Comparisons of Daily Sea Surface Temperatures and In-Situ Temperatures in the Coastal Regions

In this study, seven, global, blended, sea surface temperature (SST) analyses, including Operational SST and Sea Ice Analysis (OSTIA), Canadian Meteorological Centre (CMC) analysis, Optimum Interpolation SST (OISST), Remote Sensing System (REMSS) analysis, Multi-scale Ultra-high Resolution SST (MURSST), Merged Satellite and In situ Data Global Daily SST (MGDSST), and Geo-Polar Blended SST (Blended SST) were conducted. In-situ temperature measurements were used for the years 2014–2018, from 35 narrowly-spaced buoys distributed along the Korean Peninsula coast, to investigate how well the SST analyses represent the temperatures at the coastal regions. Contrary to the overall accuracy of the SSTs in the global ocean and offshore regions, the root-mean-square errors for the analyses were relatively large over 1.27 K. Specifically, all SST analyses resulted in warm biases over 0.31 K, which became quite distinctive in the western and the southwestern coastal regions. Investigation of the errors identified relationships with the coastal zones of vigorous tidal mixing, shallow bathymetry, and absence of microwave measurements. Overall, temporal wavelet coherency between in-situ measurements and SST products revealed high coherency of greater than 0.8 in periods longer than 180 days, however, low coherency (<0.5) in the period shorter than 10 days was observed. Inter-comparisons between the SST analyses illustrated clear spatial differences in the correlations at both the coastal regions, along the southwestern coast of the Korean Peninsula and in the frontal regions, and in the marginal seas of the Northwest Pacific. Overall, the results emphasized on the importance of using real-time in-situ measurements as much as possible, to overcome the increasing SST errors in coastal regions.

Concordance Modeling With a Gold Standard for Variables From the Three-Parameter Gamma Distribution

A way to compare two or more measurements for the same random variable can be achieved by using a negligible error reference measurement, which is called the gold standard, obtained by consolidated measurement methods. This paper presents a new methodology for comparing measurements in the presence of a gold standard with random variables from the multivariate three-parameter (shape, scale, and location) gamma distribution. The errors between gold standard measures and approximate measures have a gamma difference distribution with the same three parameters of the gamma distribution. The concordance measurements were obtained by mean of a coefficient, which measures the degree of agreement as a ratio between the variances of the gold standard and the errors. The developed methodology is illustrated with climatic data which is divided into four ranges. The measurements analyzed are rainfall forecasts of the following four national centers: Canadian Meteorological Center (CMC), European Center for Medium-Range Weather Forecasts (ECMWF), National Centers for Environmental Prediction (NCEP), and Center for Weather Forecasting and Climate Studies (CPTEC). The forecast range was 240 hours for the West mesoregion of Paraná – Brazil, and in the October 1–March 31 period of the 2010/2011 –2015/2016 harvest years. The period was selected because it is related to soybean crop development in the region and because several crop estimation models use rainfall forecast data in this period. The methodology applied spatially indicated the center to be selected in each geographical location according to each rainfall range interval. The gamma model fit well with the data and is an alternative to the normal one for modelling rainfall, in particular to estimate concordances between rainfall forecasts and the gold standard, which are used to improve the selection of rainfall forecast centers.

Coupled Stratospheric Chemistry–Meteorology Data Assimilation. Part II: Weak and Strong Coupling

We examine data assimilation coupling between meteorology and chemistry in the stratosphere from both weak and strong coupling strategies. The study was performed with the Canadian operational weather prediction Global Environmental Multiscale (GEM) model coupled online with the photochemical stratospheric chemistry model developed at the Belgian Institute for Space Aeronomy, described in Part I. Here, the Canadian Meteorological Centre’s operational variational assimilation system was extended to include errors of chemical variables and cross-covariances between meteorological and chemical variables in a 3D-Var configuration, and we added the adjoint of tracer advection in the 4D-Var configuration. Our results show that the assimilation of limb sounding observations from the MIPAS instrument on board Envisat can be used to anchor the AMSU-A radiance bias correction scheme. Additionally, the added value of limb sounding temperature observations on meteorology and transport is shown to be significant. Weak coupling data assimilation with ozone–radiation interaction is shown to give comparable results on meteorology whether a simplified linearized or comprehensive ozone chemistry scheme is used. Strong coupling data assimilation, using static error cross-covariances between ozone and temperature in a 3D-Var context, produced inconclusive results with the approximations we used. We have also conducted the assimilation of long-lived species observations using 4D-Var to infer winds. Our results showed the added value of assimilating several long-lived species, and an improvement in the zonal wind in the Tropics within the troposphere and lower stratosphere. 4D-Var assimilation also induced a correction of zonal wind in the surf zone and a temperature bias in the lower tropical stratosphere.

Analysing the contribution of snow water equivalent to the terrestrial water storage over Canada

AbstractIn this study, the spatial and temporal variabilities of terrestrial water storage anomaly (TWSA) and snow water equivalent anomaly (SWEA) information obtained from the Gravity Recovery and Climate Experiment (GRACE) twin satellites data were analysed in conjunction with multisource snow products over several basins in the Canadian landmass. Snow water equivalent (SWE) data were extracted from three different sources: Global Snow Monitoring for Climate Research version 2 (GlobSnow2), Advanced Microwave Scanning Radiometer‐Earth Observing System (AMSR‐E), and Canadian Meteorological Centre (CMC). The objective of the study was to understand whether SWE variations have a significant contribution to terrestrial water storage anomalies in the Canadian landmass. The period was considered from December 2002 to March 2011. Significant relationships were observed between TWSA and SWEA for most of the 15 basins considered (53% to 80% of the basins, depending on the SWE products considered). The best results were obtained with the CMC SWE products compared with satellite‐based SWE data. Stronger relationships were found in snow‐dominated basins (Rs > = 0.7), such as the Liard [root mean square error (RMSE) = 21.4 mm] and Peace Basins (RMSE = 26.76 mm). However, despite high snow accumulation in the north of Quebec, GRACE showed weak or insignificant correlations with SWEA, regardless of the data sources. The same behaviour was observed in the Western Hudson Bay basin. In both regions, it was found that the contribution of non‐SWE compartments including wetland, surface water, as well as soil water storages has a significant impact on the variations of total storage. These components were estimated using the Water‐Global Assessment and Prognosis Global Hydrology Model (WGHM) simulations and then subtracted from GRACE observations. The GRACE‐derived SWEA correlation results showed improved relationships with three SWEA products. The improvement is particularly important in the sub‐basins of the Hudson Bay, where very weak and insignificant results were previously found with GRACE TWSA data. GRACE‐derived SWEA showed a significant relationship with CMC data in 93% of the basins (13% more than GRACE TWSA). Overall, the results indicated the important role of SWE on terrestrial water storage variations.