Search

The Western Arctic Linkage Experiment (WALE) is aimed at understanding the role of high-latitude terrestrial ecosystems in the response of the Arctic system to global change through collection and comparison of climate datasets and model results. In this paper, a spatiotemporal approach is taken to compare and validate model results from the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) with commonly used analysis and reanalysis datasets for monthly averages of temperature and precipitation in 1992–2000 and for a study area at 55°–65°N, 160°–110°W in northwestern Canada and Alaska. Objectives include a quantitative assessment of similarity between datasets and climate model fields, and identification of geographic areas and seasons that are problematic in modeling, with potential causes that may aid in model improvement. These are achieved by application of algebraic similarity mapping, a simple yet effective method for synoptic analysis of many (here, 45) different spatial datasets, maps, and models. Results indicate a dependence of model–data similarity on seasonality, on climate variable, and on geographic location. In summary, 1) similarity of data and models is better for temperature than for precipitation; and 2) modeling of summer precipitation fields, and to a lesser extent, temperature fields, appears more problematic than that of winter fields. The geographic distribution of areas with best and worst agreement shifts throughout the year, with generally better agreement between maps and models in the northeastern and northern inland areas than in topographically complex and near-coastal areas. The study contributes to an understanding of the geographic complexity of the Arctic system and modeling its diverse climate. [ABSTRACT FROM AUTHOR]

A heavy rainfall event over a 2-h period on 8 July 2013 caused significant flash flooding in the city of Toronto and produced 126 mm of rain accumulation at a gauge located near the Toronto Pearson International Airport. This paper evaluates the quantitative precipitation estimates from the nearby King City C-band dual-polarized radar (WKR). Horizontal reflectivity Z and differential reflectivity ZDR were corrected for attenuation using a modified ZPHI rain profiling algorithm, and rain rates R were calculated from R( Z) and R( Z, ZDR) algorithms. Specific differential phase KDP was used to compute rain rates from three R( KDP) algorithms, one modified to use positive and negative KDP, and an R( KDP, ZDR) algorithm. Additionally, specific attenuation at horizontal polarization A was used to calculate rates from the R( A) algorithm. High-temporal-resolution rain gauge data at 44 locations measured the surface rainfall every 5 min and produced total rainfall accumulations over the affected area. The nearby NEXRAD S-band dual-polarized radar at Buffalo, New York, provided rain-rate and storm accumulation estimates from R( Z) and S-band dual-polarimetric algorithm. These two datasets were used as references to evaluate the C-band estimates. Significant radome attenuation at WKR overshadowed the attenuation correction techniques and resulted in poor rainfall estimates from the R( Z) and R( Z, ZDR) algorithms. Rainfall estimation from the Brandes et al. R( KDP) and R( A) algorithms were superior to the other methods, and the derived storm total accumulation gave biases of 2.1 and −6.1 mm, respectively, with correlations of 0.94. The C-band estimates from the Brandes et al. R( KDP) and R( A) algorithms were comparable to the NEXRAD S-band estimates. [ABSTRACT FROM AUTHOR]

River flow monitoring is a critical task for land management, agriculture, fishery, industry, and other concerns. Herein, a robust least squares triple cross-wavelet analysis is proposed to investigate possible relationships between river flow, temperature, and precipitation in the time–frequency domain. The Athabasca River basin (ARB) in Canada is selected as a case study to investigate such relationships. The historical climate and river flow datasets since 1950 for three homogeneous subregions of the ARB were analyzed using a traditional multivariate regression model and the proposed wavelet analysis. The highest Pearson correlation (0.87) was estimated between all the monthly averaged river flow, temperature, and accumulated precipitation for the subregion between Hinton and Athabasca. The highest and lowest correlations between climate and river flow were found to be during the open warm season and cold season, respectively. Particularly, the highest correlations between temperature, precipitation, and river flow were in May (0.78) for Hinton, July (0.54) for Athabasca, and September (0.44) for Fort McMurray. The new wavelet analysis revealed significant coherency between annual cycles of climate and river flow for the three subregions, with the highest of 33.7% for Fort McMurray and the lowest of 4.7% for Hinton with more coherency since 1991. The phase delay analysis showed that annual and semiannual cycles of precipitation generally led the ones in river flow by a few weeks mainly for the upper and middle ARB since 1991. The climate and river flow anomalies were also demonstrated using the baseline period 1961–90, showing a significant increase in temperature and decrease in precipitation since 1991 for all the three subregions. Unlike the multivariate regression, the proposed wavelet method can analyze any hydrometeorological time series in the time–frequency domain without any need for resampling, interpolation, or gap filling. [ABSTRACT FROM AUTHOR]

Since mid-February 2013, the ensemble Kalman filter (EnKF) in operation at the Canadian Meteorological Centre (CMC) has been using a 600 × 300 global horizontal grid and 74 vertical levels. This yields 5.4 × 107 model coordinates. The EnKF has 192 members and uses seven time levels, spaced 1 h apart, for the time interpolation in the 6-h assimilation window. It follows that over 7 × 1010 values are required to specify an ensemble of trial field trajectories. This paper focuses on numerical and computational aspects of the EnKF. In response to the increasing computational challenge posed by the ever more ambitious configurations, an ever larger fraction of the EnKF software system has gradually been parallelized over the past decade. In a strong scaling experiment, the way in which the execution time decreases as larger numbers of processes are used is investigated. In fact, using a substantial fraction of one of the CMC's computers, very short execution times are achieved. As it would thus appear that the CMC's computers can handle more demanding configurations, weak scaling experiments are also performed. Here, both the size of the problem and the number of processes are simultaneously increased. The parallel algorithm responds well to an increase in either the number of ensemble members or the number of model coordinates. A substantial increase (by an order of magnitude) in the number of assimilated observations would, however, be more problematic. Thus, to the extent that this depends on computational aspects, it appears that the meteorological quality of the Canadian operational EnKF can be further improved. [ABSTRACT FROM AUTHOR]

This paper compares three existing Palmer Drought Severity Index (PDSI) formulations for simulating summer moisture variability in western Canada and a preliminary analysis of climate change impacts on summer moisture anomalies. The three models considered are Palmer's original algorithm (orPDSI), the self-calibrating PDSI (scPDSI), and a version modified for Canadian Prairie conditions (cpPDSI). In all formulations, potential evapotranspiration was parameterized by the Penman-Monteith method instead of the traditional Thornthwaite method. The scPDSI was used as a benchmark for evaluation as it is more appropriate for comparing drought severity of diverse climates. The results confirm that orPDSI produces inflated drought statistics as compared to scPDSI, whereas cpPDSI produced more conservative drought statistics than scPDSI. On the basis of results from scPDSI, historical moisture availability in the Canadian Prairies has shown a significant downward trend since 1950 at the 5% level, whereas southern British Columbia has shown a significant increasing trend. No discernible trend was found in the northern parts of the study area. These results were corroborated by trends in annual precipitation and summer temperature over the respective regions. When scPDSI parameters were calibrated using historical climate data, simulations for the 2050s using climate change scenarios from the Intergovernmental Panel on Climate Change Fourth Assessment Report (IPCC AR4) showed increases in summer moisture deficit relative to the 1961-90 baseline. However, projecting the extent to which the frequency of extreme drought and/or wet spell categories will change is not trivial since the computation of scPDSI is tied to the definition of the frequency of extreme events. [ABSTRACT FROM AUTHOR]

An algorithm is developed to determine the flight icing threat to aircraft utilizing quantitative information on clouds derived from meteorological satellite data as input. Algorithm inputs include the satellite-derived cloud-top temperature, thermodynamic phase, water path, and effective droplet size. The icing-top and -base altitude boundaries are estimated from the satellite-derived cloud-top and -base altitudes using the freezing level obtained from numerical weather analyses or a lapse-rate approach. The product is available at the nominal resolution of the satellite pixel. Aircraft pilot reports (PIREPs) over the United States and southern Canada provide direct observations of icing and are used extensively in the algorithm development and validation on the basis of correlations with Geostationary Operational Environmental Satellite imager data. Verification studies using PIREPs, Tropospheric Airborne Meteorological Data Reporting, and NASA Icing Remote Sensing System data indicate that the satellite algorithm performs reasonably well, particularly during the daytime. The algorithm is currently being run routinely using data taken from a variety of satellites across the globe and is providing useful information on icing conditions at high spatial and temporal resolutions that are unavailable from any other source. [ABSTRACT FROM AUTHOR]

The main task of this study is to introduce a statistical postprocessing algorithm to reduce the bias in the National Centers for Environmental Prediction (NCEP) and Meteorological Service of Canada (MSC) ensemble forecasts before they are merged to form a joint ensemble within the North American Ensemble Forecast System (NAEFS). This statistical postprocessing method applies a Kalman filter type algorithm to accumulate the decaying averaging bias and produces bias-corrected ensembles for 35 variables. NCEP implemented this bias-correction technique in 2006. NAEFS is a joint operational multimodel ensemble forecast system that combines NCEP and MSC ensemble forecasts after bias correction. According to operational statistical verification, both the NCEP and MSC bias-corrected ensemble forecast products are enhanced significantly. In addition to the operational calibration technique, three other experiments were designed to assess and mitigate ensemble biases on the model grid: a decaying averaging bias calibration method with short samples, a climate mean bias calibration method, and a bias calibration method using dependent data. Preliminary results show that the decaying averaging method works well for the first few days. After removing the decaying averaging bias, the calibrated NCEP operational ensemble has improved probabilistic performance for all measures until day 5. The reforecast ensembles from the Earth System Research Laboratory's Physical Sciences Division with and without the climate mean bias correction were also examined. A comparison between the operational and the bias-corrected reforecast ensembles shows that the climate mean bias correction can add value, especially for week-2 probability forecasts. [ABSTRACT FROM AUTHOR]