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Jason A. Milbrandt
,
Stéphane Bélair
,
Manon Faucher
,
Marcel Vallée
,
Marco L. Carrera
, and
Anna Glazer

Abstract

Since 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), fractional cloud cover, and also against upper-air soundings, all using standard metrics. Although the predictions of some fields were degraded in some specific regions, the HRDPS generally outperformed the operational system for a majority of the scores. The evaluation illustrates the added value of the 2.5-km model and the potential for improved numerical guidance for the prediction of high-impact weather.

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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
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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