Abstract

For our student seminar, we have prepared something special. When
discussing our respective talks, we realized that we were touching many
similar concepts from different points of view. Instead of working out a
bridge between our presentation, we have interlaced them in order to
make a story. We think this will make it easier (and more interesting)
to understand the concepts presented. For us, it make a nice change from
the more formal conference presentations that we are accustomed to.

In the same spirit, here is our common abstract,

*Mesoscale prediction of precipitation: current status and future work*

It is widely accepted that whereas the performance of Numerical Weather
Prediction (NWP) models is continuously improving, precipitation still
remains very difficult to forecast and quantitative precipitation
forecasting (QPF) skill is generally low. This talk discusses the
ability of current generation mesoscale models (with dx~1km) to forecast
rainfall. Emphasis is put on the main factors affecting forecast quality
and on the methods that could improve QPF.

Through the evaluation of high-resolution ensemble
precipitation forecasts it is shown that models have generally very
little skill in forecasting rainfall at scales lower than 100km.
Furthermore, while ensemble methods can increase predictability at
scales larger than 100km, for small scales, the spread is too large to
provide useful forecasts.

At storm scales, assimilation of radar observations has the potential to
improve model predictions. So far, demonstrating the improvements
brought by assimilation has proven very challenging as forecasts show
great sensitivity to small errors in initial conditions. This is
especially true for humidity, which is not corrected significantly
through assimilation. As a solution to this problem, an alternative
method for the assimilation of radar observations based on a combination
of variational techniques and statistical analysis of model output is
discussed here.

Another pathway for the improvement of forecasts is through the use of
more accurate model physics. However, sensitivity tests show that
despite large dependence of results on various model parameters at small
scales, no single parameter explains the largest forecast errors.

The work presented here seems to indicate the existence of
a critical spatial scale situated around 100km. Above this scale,
forecasting results are satisfactory, while below it QPF skill is poor.
The effect of radar data assimilation is limited to scales smaller than
the critical scale such that improvements due to assimilation are
expected to be short-lived.

HDFView Screenshot

HDFView is a visual tool for browsing and editing HDF4 and HDF5 files. Using HDFView, you can:

• >view a file hierarchy in a tree structure
• create new file, add or delete groups and datasets
• view and modify the content of a dataset
• add, delete and modify attributes
• replace I/O and GUI components such as table view, image view and metadata view

Abstract

A brief history of National Severe Storms Laboratory and the evolution of research activities there. The use of Doppler radar and GOES satellite data as an observation tool for detecting thunderstorm initiation and precipitation systems, land-use interaction with the boundary layer, monitoring surface wetness and wildfires, and challenges in numerical weather forecasting will be discussed. Ideas for collaboration with projects will be explored.

We wish you and your family happy holidays!

The new AOS Wiki Theme

Remember, you can login with your AOS account to make any changes you feel may be beneficial to the department. If you have any questions or concerns, please contact support@meteo.mcgill.ca.

Courtesy of Exelis Visualization

10 license for IDL 8.2 are now available departmentally. Special thanks the following individuals for their contributions:

• Prof. Parisa Ariya
• Prof. Peter Bartello
• Prof. Frédéric Fabry
• Prof. John Gyakum
• Prof. Daniel Kirshaum
• Prof. Peter Yau

To use idl, you must be logged into a departmental 64-bit Linux system. Once logged in, you may issue:

idl to start IDL in the terminal

idlde to start IDL graphically

If you experience any difficulties, please contact support@meteo.mcgill.ca

Annual-mean climatology (1979-2005) of 100-hPa temperature from a) ERA-Interim and b) ensemble of CMIP5 models. White contours denote OLR from observation and model ensemble respectively. c) Taylor diagram of the temperature field within 15S-15N for individual models (open and closed circles) and their ensemble (cross).

Thermal characteristics of the tropical tropopause layer in CMIP5 models: historical simulations

The climatology and variability of temperatures in the tropical tropopause layer are investigated in 16 Coupled Model Intercomparison Project Phase 5 (CMIP5) models for historical simulations. The climatology of 100-hPa temperatures compare well with ERA-Interim reanalysis. The models possess reasonable temperature minima in the deep tropics, but some models also have a warm bias or a bias in the location of the temperature minima. The CMIP5 models generally capture the phase of the seasonal cycle in 100-hPa temperatures, but the amplitude of the seasonal cycle varies greatly among models. The interannual variability in 100-hPa temperature is associated with the El Niño-Southern Oscillation (ENSO) and volcanic forcing in observation and CMIP5 models. Most of models successfully capture the ENSO-related large scale response, but the response to volcanic forcing is overestimated in many models. On intraseasonal timescales, observed and modeled variability is dominated by equatorial waves (Kelvin, inertio-gravity, and mixed Rossby-gravity waves) and the Madden-Julian Oscillation (MJO). Most models show variability related to the equatorial waves, but significant biases are found in the phase speeds of the waves when compared to ERA-Interim. The MJO signature is weak and non-distinguishable from the Kelvin wave power in most CMIP5 models.

Energy dissipation in viscous-plastic sea ice models

Energy dissipation in viscous-plastic sea ice models by Amélie Bouchat

Most current sea ice models are based on the viscous approximation introduced by Hibler (1979). These viscous-plastic (VP) models approximate the small elastic deformations by the viscous deformations of a creeping flow. The resulting numerical treatment is much more simple than in earlier sea ice models, but viscous deformations introduce a non-physical energy sink in the model since they are not reversible as the elastic deformations. The project is aimed at assessing the consequences, physically speaking, of this non-physical energy dissipation in the models and therefore verifying the validity of the VP approximation. The dissipation is studied using the kinetic energy balance that is derived using the continuity and momentum equations for sea ice. This analysis allows us to evaluate the different terms of the balance and look at the relative importance of the viscous dissipation. The results will be presented for a 40km-resolution run of 1 year. The dependence of the viscous dissipation on the number of Newton loops of the numerical solver of the momentum equation is also studied. Preliminary results at 20km-resolution will also be shown.

These images show simulated fields of trade-wind convection impinging on an idealized island ridge with a height of 500 m. Conditions for these cases are derived from field campaigns (BOMEX and RICO) over the western Atlantic Ocean. The ocean is depicted in blue, the island in green, and the cloud field (the 0.1 g/kg isosurface) in gray. The clouds clearly become more vigorous and deeper over the island ridge. Note also that individual cloud complexes are apparent with much larger sizes than any oceanic clouds. These larger clouds are the main precipitation producers over the island.

Cumulus cloud fields are typically studied in idealized environments that are horizontally homogenous over large areas. In reality, however, these clouds fields typically exhibit substantial variability on the mesoscale (scales of 1-1000 km), which appears to violate the assumption of horizontal homogeneity. Rather than forming in random locations, clouds often cluster in specific regions. This variability may be associated with feedbacks from the clouds onto the larger-scale flow (e.g., cold pools, gravity waves) and/or external forcings imposed on the cloud field. This study investigates the latter mechanism, specifically the impact of mountainous islands on preexisting cloud fields. This is carried out through an analysis of radar, rain-gauge, and aircraft observations over the Caribbean island of Dominica and through idealized large-eddy simulations. The observations document an intense enhancement in cloud coverage and precipitation over Dominica, which is large in virtually all synoptic environments. The simulations provide a physical basis for interpreting the observations. They reveal two mechanisms for the large enhancement in cloud vigour: (1) an increase in cloud buoyancy as moist air ascends alongside dry air with different adiabatic lapse rates and (2) an increase in the mean size of cumulus clouds, which weakens the fractional entrainment of environmental air. Although both mechanisms increase the buoyancy and vertical velocity of convective cores, the latter is potentially more important due to the increase in cloud liquid water, which stimulates faster accretional growth of precipitation particles. For more information see the following references:

• Kirshbaum, D. J. and A. L. M. Grant, 2012: Invigoration of cumulus cloud fields by mesoscale ascent. Q. J. R. Meteorol. Soc., in press, DOI: 10.1002/qj.1954
• Smith, R. B., J. R. Minder, A. D. Nugent, T. Storelvmo, D. J. Kirshbaum, R. Warren, N. Lareau, P. Palany, A. James, and J. French, 2012: Orographic precipitation in the tropics: the Dominica Experiment. Bull. Amer. Meteor. Soc., 93, 1567-1579.
• Kirshbaum, D. J. and R. B. Smith, 2009: Orographic precipitation in the tropics: large-eddy simulations and theory. J. Atmos. Sci., 66: 2559-2578.

About the author

Dr. Kirshbaum is an Assistant Professor in the Atmospheric and Oceanic Sciences Department at McGill. His research focuses on the mechanisms and predictability of atmospheric convection and other mesoscale phenomena.

Please join us tomorrow in Burnside 934 at 14:35 for a student seminar by Melissa Gervais. Abstract follows.

How Well is the Distribution of Precipitation Represented? Part I: Impacts of Station Density and Resolution Changes on Gridded Station Data

Precipitation is one of the most important variables to predict in future climate change owing to the socio-economic implications for water resources. However, it has historically been a very challenging variable for climate models to predict. Newer versions of Community Climate System Model (CCSM) from the National Center for Atmospheric Research (NCAR) have seen great improvements in their representation of the distribution of precipitation, with results now very close to observations (Gent 2011). The accuracy of precipitation observations used to validate the GCM output is thus becoming increasingly important. Results will be presented from the first of two studies on examining the ability of observations, reanalysis, the CCSM4 fully coupled model, and the NCAR Community Atmosphere Model (CAM5), to represent the distribution of precipitation. Here, we focus on the accuracy of interpolating station data in terms of the method of interpolation and the station density.

Station data from the Global Historical Climatology Network – Daily Version 1.0, within the United States, will be used to create and test gridded precipitation products. The goal is firstly to examine what the impact of gridding station data is on the precipitation statistics and whether the gridding method used is important. Secondly, an experiment will be conducted to determine how dense an observation network needs to be, in different climatic regions, in order to produce an accurate distribution of precipitation. This allows us to identify regions where station density is not high enough to trust the gridded precipitation data for validating GCMs.