scholarly journals Daily Precipitation Fields Modeling across the Great Lakes Region (Canada) by Using the CFSR Reanalysis

2018 ◽  
Vol 57 (10) ◽  
pp. 2419-2438 ◽  
Author(s):  
Dikra Khedhaouiria ◽  
Alain Mailhot ◽  
Anne-Catherine Favre
Keyword(s):  
Great Lakes ◽  
Linear Model ◽  
Random Fields ◽  
Generalized Linear Model ◽  
Daily Precipitation ◽  
Weather Prediction ◽  
Gaussian Random Fields ◽  
Great Lakes Region ◽  
Spatiotemporal Random Fields ◽  
Model Output Statistics

AbstractReanalyses, generated by numerical weather prediction methods assimilating past observations, provide consistent and continuous meteorological fields for a specific period. In regard to precipitation, reanalyses cannot be used as a climate proxy of the observed precipitation, as biases and scale mismatches exist between the datasets. In the present study, a stochastic model output statistics (SMOS) approach combined with meta-Gaussian spatiotemporal random fields was employed to cope with these caveats. The SMOS is based on the generalized linear model (GLM) and the vector generalized linear model (VGLM) frameworks to model the precipitation occurrence and intensity, respectively. Both models use the Climate Forecast System Reanalysis (CFSR) precipitation as covariate and were locally calibrated at 173 sites across the Great Lakes region. Combined with meta-Gaussian random fields, the GLM and VGLM models allowed for the generation of spatially coherent daily precipitation fields across the region. The results indicated that the approach corrected systematic biases and provided an accurate spatiotemporal structure of daily precipitation. Performances of selected precipitation indicators from the joint Commission for Climatology (CCl)/CLIVAR/JCOMM Expert Team on Climate Change Detection and Indices (ETCCDI) were good and were systematically improved when compared to CFSR.

scholarly journals Lake breezes in the southern Great Lakes region and their influence during BAQS-Met 2007

2011 ◽  
Vol 11 (2) ◽  
pp. 3579-3626 ◽  
Author(s):  
D. M. L. Sills ◽  
J. R. Brook ◽  
I. Levy ◽  
P. A. Makar ◽  
J. Zhang ◽  
...  
Keyword(s):  
Air Quality ◽  
Great Lakes ◽  
Weather Prediction ◽  
Great Lakes Region ◽  
Lake Breeze ◽  
Moist Convection ◽  
Study Region ◽  
Synoptic Wind ◽  
Lake Breezes ◽  
Deep Moist Convection

Abstract. Mesoscale observations from the BAQS-Met field experiment during the summer of 2007 were integrated and manually analyzed in order to identify and characterize lake breezes in the southern Great Lakes region of North America, and assess their potential impact on air quality. Lake breezes were found to occur on 90% of study days, often occurring in conditions previously thought to impede their development. They affected all parts of the study region, including southwestern Ontario and nearby portions of southeast Michigan and northern Ohio, occasionally penetrating inland from 100 km to over 200 km. Occurrence rates and penetration distances were found to be higher than previously reported in the literature. This more accurate depiction of observed lake breezes allows a better understanding of their influence on the production and transport of pollutants in this region. The observational analyses were compared with output from subsequent runs of a high-resolution numerical weather prediction model. The model accurately predicted lake breeze occurrence in a variety of synoptic wind regimes, but selected cases showed substantial differences in the detailed timing and location of lake-breeze fronts, and with the initiation of deep moist convection. Knowledge of such strengths and weaknesses will assist with interpretation of results from air quality modelling driven by this meteorological model.

scholarly journals Lake breezes in the southern Great Lakes region and their influence during BAQS-Met 2007

2011 ◽  
Vol 11 (15) ◽  
pp. 7955-7973 ◽  
Author(s):  
D. M. L. Sills ◽  
J. R. Brook ◽  
I. Levy ◽  
P. A. Makar ◽  
J. Zhang ◽  
...  
Keyword(s):  
Air Quality ◽  
Great Lakes ◽  
Weather Prediction ◽  
Great Lakes Region ◽  
Lake Breeze ◽  
Moist Convection ◽  
Study Region ◽  
Synoptic Wind ◽  
Lake Breezes ◽  
Deep Moist Convection

Abstract. Meteorological observations from the BAQS-Met field experiment during the summer months of 2007 were integrated and manually analyzed in order to identify and characterize lake breezes in the southern Great Lakes region of North America, and assess their potential impact on air quality. Lake breezes occurred frequently, with one or more lake breezes identified on 90 % of study days. They affected all parts of the study region, including southwestern Ontario and nearby portions of southeast Lower Michigan and northern Ohio, with lake-breeze fronts occasionally penetrating from 100 km to over 200 km inland. Occurrence rates and penetration distances were found to be higher than previously reported in the literature. This comprehensive depiction of observed lake breezes allows an improved understanding of their influence on the transport, dispersion, and production of pollutants in this region. The observational analyses were compared with output from subsequent runs of a high-resolution numerical weather prediction model. The model accurately predicted lake breeze occurrence and type in a variety of synoptic wind regimes, but selected cases showed substantial differences in the detailed timing and location of lake-breeze fronts, and with the initiation of deep moist convection. Knowledge of such strengths and weaknesses aids in the interpretation of results from air quality models driven by this meteorological model.

scholarly journals Prediction of Lake-Effect Snow Using Convection-Allowing Ensemble Forecasts and Regional Data Assimilation

2017 ◽  
Vol 32 (5) ◽  
pp. 1727-1744 ◽  
Author(s):  
Seth Saslo ◽  
Steven J. Greybush
Keyword(s):  
Data Assimilation ◽  
Great Lakes ◽  
Forecast Error ◽  
Weather Prediction ◽  
The United States ◽  
Ensemble Prediction ◽  
Great Lakes Region ◽  
Prediction System ◽  
Ensemble Prediction System ◽  
Lake Effect

Abstract Lake-effect snow (LES) is a cold-season mesoscale convective phenomenon that can lead to significant snowfall rates and accumulations in the Great Lakes region of the United States. While limited-area numerical weather prediction models have shown skill in prediction of warm-season convective storms, forecasting the sharp nature of LES precipitation timing, intensity, and location is difficult because of model error and initial and boundary condition uncertainties. Ensemble forecasting can incorporate and quantify some sources of forecast error, but ensemble design must be considered. This study examines the relative contributions of forecast uncertainties to LES forecast error using a regional convection-allowing data assimilation and ensemble prediction system. Ensembles are developed using various methods of perturbations to simulate a long-lived and high-precipitation LES event in December 2013, and forecast performance is evaluated using observations including those from the Ontario Winter Lake-Effect Systems (OWLeS) campaign. Model lateral boundary conditions corresponding to weather conditions beyond the Great Lakes region play an influential role in LES precipitation forecasts and their uncertainty, as evidenced by ensemble spread, particularly at lead times beyond one day. A strong forecast dependence on regional initial conditions was shown using data assimilation. This sensitivity impacts the timing and intensity of predicted precipitation, as well as band location and orientation assessed with an object-based verification approach, giving insight into the time scales of practical predictability of LES. Overall, an assimilation-cycling convection-allowing ensemble prediction system could improve future lake-effect snow precipitation forecasts and analyses and can help quantify and understand sources of forecast uncertainty.

scholarly journals Statistical modelling of rainfall-induced shallow landsliding using static predictors and numerical weather predictions: preliminary results

2015 ◽  
Vol 15 (1) ◽  
pp. 75-95 ◽  
Author(s):  
V. Capecchi ◽  
M. Perna ◽  
A. Crisci
Keyword(s):  
Linear Model ◽  
Random Forests ◽  
Generalized Linear Model ◽  
Weather Prediction ◽  
Statistical Modelling ◽  
Rainfall Event ◽  
Added Value ◽  
Limited Area ◽  
Numerical Weather ◽  
Hourly Rainfall

Abstract. Our study is aimed at estimating the added value provided by Numerical Weather Prediction (NWP) data for the modelling and prediction of rainfall-induced shallow landslides. We implemented a quantitative indirect statistical modelling of such phenomena by using, as input predictors, both geomorphological, geological, climatological information and numerical data obtained by running a limited-area weather model. Two standard statistical techniques are used to combine the predictor variables: a generalized linear model and Breiman's random forests. We tested these models for two rainfall events that occurred in 2011 and 2013 in Tuscany region (central Italy). Modelling results are compared with field data and the forecasting skill is evaluated by mean of sensitivity–specificity receiver operating characteristic (ROC) analysis. In the 2011 rainfall event, the random forests technique performs slightly better than generalized linear model with area under the ROC curve (AUC) values around 0.91 vs. 0.84. In the 2013 rainfall event, both models provide AUC values around 0.7. Using the variable importance output provided by the random forests algorithm, we assess the added value carried by numerical weather forecast. The main results are as follows: (i) for the rainfall event that occurred in 2011 most of the NWP data, and in particular hourly rainfall intensities, are classified as "important" and (ii) for the rainfall event that occurred in 2013 only NWP soil moisture data in the first centimetres below ground is found to be relevant for landslide assessment. In the discussions we argue how these results are connected to the type of precipitation observed in the two events.

On using a generalized linear model to downscale daily precipitation for the center of Portugal: an analysis of trends and extremes

2014 ◽  
Vol 120 (1-2) ◽  
pp. 147-158 ◽  
Author(s):  
Mário Pulquério ◽  
Pedro Garrett ◽  
Filipe Duarte Santos ◽  
Maria João Cruz
Keyword(s):  
Linear Model ◽  
Generalized Linear Model ◽  
Daily Precipitation

scholarly journals A Statistical Parameter Correction Technique for WRF Medium-Range Prediction of Near-Surface Temperature and Wind Speed Using Generalized Linear Model

Atmosphere ◽  
2018 ◽  
Vol 9 (8) ◽  
pp. 291 ◽  
Author(s):  
Jinmyeong Jeong ◽  
Seung-Jae Lee
Keyword(s):  
Wind Speed ◽  
Linear Model ◽  
Generalized Linear Model ◽  
Pearson Correlation ◽  
Weather Prediction ◽  
Statistical Parameter ◽  
Test Site ◽  
Near Surface ◽  
Correction Technique ◽  
Parameter Correction

A statistical post-processing method was developed to increase the accuracy of numerical weather prediction (NWP) and simulation by matching the daily distribution of predicted temperatures and wind speeds using the generalized linear model (GLM) and parameter correction, considering an increase in model bias when the range of the prediction time lengthens. The Land Atmosphere Modeling Package Weather Research and Forecasting model, which provides 12-day agrometeorological predictions for East Asia, was employed from May 2017 to April 2018. Training periods occurred one month prior to and after the test period (12 days). A probabilistic consideration accounts for the relatively short training period. Based on the total and monthly root mean square error values for each test site, the results show an improvement in the NWP accuracy after bias correction. The spatial distributions in July and January were compared in detail. It was also shown that the physical consistency between temperature and wind speed was retained in the correction procedure, and that the GLM exhibited better performance than the quantile matching method based on monthly Pearson correlation comparison. The characteristics of coastal and mountainous sites are different from inland automatic weather stations, indicating that supplements to cover these distinctive topographic locations are necessary.

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