scholarly journals Spatial Forecasts of Maximum Hail Size Using Prognostic Model Soundings and HAILCAST

2006 ◽  
Vol 21 (2) ◽  
pp. 206-219 ◽  
Author(s):  
Julian C. Brimelow ◽  
Gerhard W. Reuter ◽  
Ron Goodson ◽  
Terrence W. Krauss
Keyword(s):  
Prognostic Model ◽  
Multiscale Model ◽  
Maximum Diameter ◽  
Small Spatial Scale ◽  
Canadian Prairies ◽  
Global Environmental ◽  
Southern Alberta ◽  
Forecast Technique ◽  
Nwp Model ◽  
Global Environmental Multiscale

Abstract Forecasting the occurrence of hail and the maximum hail size is a challenging problem. This paper investigates the feasibility of producing maps of the forecast maximum hail size over the Canadian prairies using 12-h prognostic soundings from an operational NWP model as input for a numerical hail growth model. Specifically, the Global Environmental Multiscale model run by the Canadian Meteorological Center is used to provide the initial data for the HAILCAST model on a 0.5° × 0.5° grid. Maps depicting maximum hail size for the Canadian prairies are generated for 0000 UTC for each day from 1 June to 31 August 2000. The forecast hail-size maps are compared with radar-derived vertically integrated liquid data over southern Alberta and surface hail reports. Verification statistics suggest that the forecast technique is skillful at identifying the occurrence of a hail day versus no-hail day up to 12 h in advance. The technique is also skillful at predicting the main threat areas. The maximum diameter of the hailstones is generally forecast quite accurately when compared with surface observations. However, the technique displays limited skill when forecasting the distribution of hail on a small spatial scale.

scholarly journals Explicit Forecasts of Hail Occurrence and Expected Hail Size Using the GEM–HAILCAST System with a Rainfall Filter

2009 ◽  
Vol 24 (4) ◽  
pp. 935-945 ◽  
Author(s):  
Julian C. Brimelow ◽  
Gerhard W. Reuter
Keyword(s):  
Steady State ◽  
Input Data ◽  
Prognostic Model ◽  
Cloud Model ◽  
Time Dependent ◽  
Special Study ◽  
Canadian Prairie ◽  
Global Environmental ◽  
Southern Alberta ◽  
Global Environmental Multiscale

Abstract HAILCAST is a numerical model developed specifically to predict the size of the largest hail reaching the ground. It consists of a steady-state cloud model combined with a time-dependent hailstone growth model. The regional version of the Canadian Global Environmental Multiscale (GEM) model is used to provide prognostic model soundings that are used as input data for HAILCAST. A map of forecasted maximum hail size is thereby obtained. Because hail is typically accompanied by rain, it would be advantageous if the GEM–HAILCAST system were to predict the occurrence of hail only in those regions where the GEM model was predicting precipitation. Hence, the utility of applying a forecast rainfall mask from the GEM model to restrict hail forecasts to those areas where rainfall is forecast during a 12-h window centered on 0000 UTC was tested. The accumulated precipitation filter is objective and integrates both the thermodynamic and dynamic output from the GEM model over many time steps. To test the utility of applying the GEM forecast precipitation mask, the masking technique was applied to HAILCAST-predicted maximum hail size maps for the three Canadian prairie provinces between 1 June and 31 August 2000. Several case studies will be presented to illustrate the usefulness of adding the precipitation mask. Verification statistics confirm that applying the rainfall mask tends to slightly reduce the false alarm ratio while still identifying the majority of hail events within a special study area over southern Alberta. The performance of the precipitation masking technique was not as effective on severe hail days, especially when attempting to identify both the occurrence and location of severe hail swaths.

scholarly journals Evaluation of GPS Precipitable Water over Canada and the IGS Network

2005 ◽  
Vol 44 (1) ◽  
pp. 153-166 ◽  
Author(s):  
Godelieve Deblonde ◽  
Stephen Macpherson ◽  
Yves Mireault ◽  
Pierre Héroux
Keyword(s):  
Precipitable Water ◽  
Multiscale Model ◽  
Geodetic Survey ◽  
Tropospheric Delay ◽  
Observation Network ◽  
Global Environmental ◽  
The Mean ◽  
The Tropics ◽  
Estimated Error ◽  
Global Environmental Multiscale

Abstract Precipitable water (PW) derived from the GPS zenith tropospheric delay (ZTD) is evaluated (as a first step toward variational data assimilation) through comparison with that of collocated radiosondes (RS_PW), operational analyses, and 6-h forecasts (from the Canadian Global Environmental Multiscale model) of the Canadian Meteorological Centre. Two sources of ZTD data are considered: 1) final ZTD (over Canada), computed by the Geodetic Survey Division (GSD) of Natural Resources Canada, and 2) final ZTD (distributed globally), obtained from the International GPS Service (IGS). The mean GSD GPS–derived PW (GPS_PW) is 14.9 mm (reflecting the relatively cold Canadian climate), whereas that of the IGS dataset is 20.8 mm. Intercomparison statistics [correlation, standard deviation (SD), and bias] between GPS_PW and RS_PW are, respectively, 0.97, 2.04 mm, and 1.35 mm for the GSD data and 0.98, 2.6 mm, and 0.67 mm for the IGS data. Comparisons of GPS_PW with 6-h forecast PW (TRIAL_PW) show slightly lower correlations and a higher SD. The increase in SD is greater for the IGS data, which is not surprising, because in regions such as the Tropics and subtropics, moisture forecasts are of a lower quality and the RS observation network is sparse. From a three-way intercomparison (IGS GPS_PW, RS_PW, and TRIAL_PW) of the SD statistics, it is found that GPS_PW has the lowest estimated PW error (≈1 mm) for PW in the 5–30-mm range. For PW greater than 30 mm, the RS_PW estimated error is ≈2 mm, and that of GPS_PW is ≈2.5 mm. The TRIAL_PW estimated error increases with PW, reaching 5.5 mm in the 40–55-mm PW range. These intercomparison results indicate that GPS_PW should be a useful source of humidity information for NWP applications.

scholarly journals GEM-AQ, an on-line global multiscale chemical weather system: model description and evaluation of gas phase chemistry processes

2007 ◽  
Vol 7 (5) ◽  
pp. 14895-14937 ◽  
Author(s):  
J. W. Kaminski ◽  
L. Neary ◽  
J. Struzewska ◽  
J. C. McConnell ◽  
A. Lupu ◽  
...  
Keyword(s):  
Multiscale Model ◽  
Global Scale ◽  
Tropospheric Chemistry ◽  
Model Description ◽  
Weather System ◽  
Gas Phase Chemistry ◽  
Global Environmental ◽  
On Line ◽  
Phase Chemistry ◽  
Global Environmental Multiscale

Abstract. Tropospheric chemistry and air quality processes were implemented on-line in the Global Environmental Multiscale model. The integrated model, GEM-AQ, has been developed as a platform to investigate chemical weather at scales from global to urban. The model was exercised for five years (2001–2005) to evaluate its ability to simulate seasonal variations and regional distributions of trace gases such as ozone, nitrogen dioxide and carbon monoxide on the global scale. The model results presented are compared with observations from satellites, aircraft measurement campaigns and balloon sondes.

scholarly journals Validation of the Global Environmental Multiscale Model (GEM) for Iran

2021 ◽  
Author(s):  
Mohammad Mohammadlou ◽  
Abdolreza Bahremand ◽  
Daniel Princz ◽  
Nicholas Kinar ◽  
Saman Razavi
Keyword(s):  
Climate Change ◽  
Data Assimilation ◽  
Arid Zone ◽  
Model Performance ◽  
Multiscale Model ◽  
Global Scale ◽  
Temperature And Precipitation ◽  
Global Environmental ◽  
Global Environmental Multiscale ◽  
Semi Arid

Abstract The Global Environmental Multiscale Model (GEM) is an integrated forecasting and data assimilation system developed by Environment and Climate Change Canada. The model is currently in operational use for data assimilation and forecasting at global 25 km to 15 km scales; regional 10 km scales over North America; and 2.5 km scales over Canada. To demonstrate the performance of the GEM model for forecasting applications, global forecast outputs of GEM at the 25 km scale were compared to temperature and precipitation datasets collected over an area of 1,648,000 km2 especially representative of the country of Iran on a daily temporal scale. Using the De Martonne method for climate classification and data from 177 meteorological stations, the country of Iran was classified into three zones: an arid zone with 87 stations; a semi-arid zone with 63 stations; and a humid zone with 27 stations. GEM model outputs were compared to observations in each of these demarcated zones. The results show good agreement between modelled and measured daily temperatures with Kling-Gupta efficiencies of 0.76, 0.71 and 0.78 in arid, semi-arid and humid regions respectively, and a moderate agreement between modelled and measured annual precipitation with 50.06%, 35.6% and 15.38% differences in arid, semi-arid and humid regions, respectively. The results also indicate that there is a significant systematic error between the elevation of the stations and the average elevation of corresponding GEM grid cells (13%). The results provide an evaluation of the model performance for Iran to be utilized for climate change applications in a regional context and can serve as a basis for the development of future high-resolution GEM model versions on a global scale.

OSIRIS observations of a tongue of NOx in the lower stratosphere at the Antarctic vortex edge: comparison with a high-resolution simulation from the Global Environmental Multiscale (GEM) model

2007 ◽  
Vol 85 (11) ◽  
pp. 1195-1207 ◽  
Author(s):  
C E Sioris ◽  
S Chabrillat ◽  
C A McLinden ◽  
C S Haley ◽  
Y J Rochon ◽  
...  
Keyword(s):  
Lower Stratosphere ◽  
Three Dimensional ◽  
Multiscale Model ◽  
Dimensional Structure ◽  
Small Scale ◽  
Breaking Wave ◽  
Austral Spring ◽  
Global Environmental ◽  
The Antarctic ◽  
Global Environmental Multiscale

Selected NOx profiles of the Antarctic lower stratosphere inferred from OSIRIS NO2 observations are presented from the austral spring of 2003. These observations show a tongue of NOx at 100 hPa, with a concentration typical of the middle stratosphere. Simulations with the Global Environmental Multiscale model show that this small-scale tongue of NOx-rich air descended into the lower stratosphere. The tongue was formed as a result of a Rossby wave breaking days earlier, transporting NOx from the pole, where larger concentrations had recently appeared, to the edge of the vortex. The three-dimensional structure of the breaking wave is illustrated in detail. PACS Nos.: 92.60.hf, 92.60.Xg, 93.30.Ca

scholarly journals Integrating NWP Forecasts and Observation Data to Improve Nowcasting Accuracy

2012 ◽  
Vol 27 (4) ◽  
pp. 938-953 ◽  
Author(s):  
Laura X. Huang ◽  
George A. Isaac ◽  
Grant Sheng
Keyword(s):  
Bias Correction ◽  
Weather Prediction ◽  
Limited Area ◽  
Observation Data ◽  
International Airport ◽  
Global Environmental ◽  
Current Observation ◽  
Dynamic Weighting ◽  
Nwp Model ◽  
Global Environmental Multiscale

Abstract This study addresses the issue of improving nowcasting accuracy by integrating several numerical weather prediction (NWP) model forecasts with observation data. To derive the best algorithms for generating integrated forecasts, different integration methods were applied starting with integrating the NWP models using equal weighting. Various refinements are then successively applied including dynamic weighting, variational bias correction, adjusted dynamic weighting, and constraints using current observation data. Three NWP models—the Canadian Global Environmental Multiscale (GEM) regional model, the GEM Limited Area Model (LAM), and the American Rapid Update Cycle (RUC) model—are used to generate the integrated forecasts. Verification is performed at two Canadian airport locations [Toronto International Airport (CYYZ), in Ontario, and Vancouver International Airport (CYVR), in British Columbia] over the winter and summer seasons. The results from the verification for four weather variables (temperature, relative humidity, and wind speed and gust) clearly show that the integrated models with new refinements almost always perform better than each of the NWP models individually and collectively. When the integrated model with innovative dynamic weighting and variational bias correction is further updated with the most current observation data, its performance is the best among all models, for all the selected variables regardless of location and season. The results of this study justify the use of integrated NWP forecasts for nowcasting provided they are properly integrated using appropriate and specifically designed rules and algorithms.

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