Search Results

You are looking at 1 - 4 of 4 items for

  • Author or Editor: Bruce Brasnett x
  • Refine by Access: All Content x
Clear All Modify Search
Bruce Brasnett

Abstract

A global analysis of in situ observations of sea surface temperature (SST) developed for use at the Canadian Meteorological Centre is described. The analysis is done on the anomaly, the departure from climatology. The anomaly plus climatology, or resulting SST, is used as the lower boundary condition by the numerical weather prediction model. Since there is no ocean model to provide a background or first-guess field for the analysis, and since anomalies are observed to persist over long periods, the background field is obtained essentially by assuming persistence of the previous anomaly. The analysis algorithm is statistical interpolation. Attention is focused on techniques to control the quality of the observations, including a technique to remove systematic errors from ship observations. The analysis resolution is 0.9° and the correlation e-folding distance is 212 km. Verification of the analysis is presented using independent data from buoys and expendable bathythermographs for a one-year period. Verification is also presented for the National Centers for Environmental Prediction (NCEP, Washington) weekly analysis and for climatology. Results indicate that the analysis has skill over climatology in all regions and skill over the NCEP weekly analysis in the North Atlantic. In the rest of the Northern Hemisphere, analysis error estimates for the two analyses are similar, while in the Southern Hemisphere the NCEP analysis is superior, probably due to its use of satellite data. It is intended that this analysis will be an essential component of a debiasing algorithm for satellite SST observations.

Full access
Bruce Brasnett

Abstract

The operational analysis of snow depth at the Canadian Meteorological Centre is described. The analysis makes use of forecasts of precipitation and analyses of screen-level temperature to estimate snowfall and snowmelt for a global domain, and assumes persistence of the mass of the snowpack between melting and/or snowfall events. In addition, wherever snow depth observations are available, these are incorporated using the method of statistical interpolation, performed every 6 h on a ⅓° grid. Correlations between two observations or between observations and grid points are taken as functions of spatial separation in both the horizontal and vertical with an e-folding distance of 120 km in the horizontal and 800 m in the vertical. Observations undergo three separate tests designed to eliminate 1) false reports of snow when none is present, 2) systematically understated snow depths, and 3) reports that violate temporal continuity. Verification of the analysis is presented using three sources of independent data. The analysis obtained when observations are withheld is compared with quality-controlled snow depth reports, with the result that the analysis shows more skill than climatology in all regions and periods examined. The analysis is also compared with Special Sensor Microwave/Imager snow cover, where it again shows more skill than climatology. Finally, a verification over a 134-week period using the NOAA weekly hemispheric snow cover product based on visible imagery is presented. Major differences between analysis and independent data are explained.

Full access
Bruce Brasnett
and
Dorina Surcel Colan

Abstract

Experiments are carried out to assess the potential contributions of two new satellite datasets, derived from the Visible Infrared Imaging Radiometer Suite (VIIRS) on board the Suomi–National Polar-Orbiting Partnership satellite and the Advanced Microwave Scanning Radiometer 2 (AMSR2) on board the Global Change Observation Mission–Water (GCOM-W) satellite, to the quality of global sea surface temperature (SST) analyses at the Canadian Meteorological Centre (CMC). The new datasets are assimilated both separately and together. Verification of the analyses against independent data shows that the VIIRS and AMSR2 datasets yield analyses with similar global average errors, with the VIIRS analysis performing better during some seasons and the AMSR2 analysis performing better in others. Seasonal cloudiness in some regions diminishes the availability of VIIRS retrievals, resulting in better performance by the AMSR2 analysis. Both datasets were assimilated together along with data from the Advanced Very High Resolution Radiometer (AVHRR), ice data, and in situ data in an updated version of the CMC analysis produced on a 0.1° grid. Verification against independent data shows that the new analysis performed very well, with global average standard deviation consistently better than that of the international Group for High Resolution SST (GHRSST) Multiproduct Ensemble (GMPE) real-time system. This analysis is shown to outperform the currently operational CMC SST analysis, with most of the improvement being due to its assimilation of the VIIRS and AMSR2 retrievals and a further small gain being due to changes to the analysis methodology (including higher resolution).

Full access
Herschel L. Mitchell
,
Cécilien Charette
,
Clément Chouinard
, and
Bruce Brasnett

Abstract

The first part of this paper presents the results of a study of the structure of the observed residuals, or differences, between radiosonde data and the short-range forecasts that are used as trial fields in an operational hemispheric data assimilation scheme. The study is based on fitting appropriate functional representations to horizontal correlations of observed height and wind residuals. Rather than represent the height residuals by the sum of a degenerate second-order autoregressive function and an additive constant to account for long-wave error, as in a previous study, we use a representation consisting of a sum of two degenerate third-order autoregressive functions of the form (1 + cr + c2r2 /3) exp(−cr), where r represents radial distance. For the wind residuals, we use the functional form that follows by geostrophy. In addition to examining the structure of the horizontal and vertical correlations, we also present other statistics relating to the performance of the data assimilation procedure, such as vertical profiles of the magnitude of the observed wind and height residuals for various regions.

In the second part of the paper, the results of the study are used as a basis for specifying interpolation statistics for the objective analysis. To evaluate the impact of the new interpolation statistics, various objective measures of analysis performance are examined and parallel 48-h forecasts are performed. It is found that significant improvements result when the new interpolation statistics are used in the data assimilation procedure.

Full access