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- Author or Editor: B. Casati x
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Abstract
The intensity-scale verification technique introduced in 2004 by Casati, Ross, and Stephenson is revisited and improved. Recalibration is no longer performed, and the intensity-scale skill score for biased forecasts is evaluated. Energy and its percentages are introduced in order to assess the bias on different scales and to characterize the overall scale structure of the precipitation fields. Aggregation of the intensity-scale statistics for multiple cases is performed, and confidence intervals are provided by bootstrapping. Four different approaches for addressing the dyadic domain constraints are illustrated and critically compared.
The intensity-scale verification is applied to the case studies of the Intercomparison of Spatial Forecast Verification Methods Project. The geometric and synthetically perturbed cases show that the intensity-scale verification statistics are sensitive to displacement and bias errors. The intensity-scale skill score assesses the skill for different precipitation intensities and on different spatial scales, separately. The spatial scales of the error are attributed to both the size of the features and their displacement. The energy percentages allow one to objectively analyze the scale structure of the fields and to understand the intensity-scale relationship. Aggregated statistics for the Storm Prediction Center/National Severe Storms Laboratory (SPC/NSSL) 2005 Spring Program case studies show no significant differences among the models’ skill; however, the 4-km simulations of the NCEP version of the Weather Research and Forecast model (WRF4 NCEP) overforecast to a greater extent than the 2- and 4-km simulations of the NCAR version of the WRF (WRF2 and WRF4 NCAR). For the aggregated multiple cases, the different approaches addressing the dyadic domain constraints lead to similar results. On the other hand, for a single case, tiling provides the most robust and reliable approach, since it smoothes the effects of the discrete wavelet support and does not alter the original precipitation fields.
Abstract
The intensity-scale verification technique introduced in 2004 by Casati, Ross, and Stephenson is revisited and improved. Recalibration is no longer performed, and the intensity-scale skill score for biased forecasts is evaluated. Energy and its percentages are introduced in order to assess the bias on different scales and to characterize the overall scale structure of the precipitation fields. Aggregation of the intensity-scale statistics for multiple cases is performed, and confidence intervals are provided by bootstrapping. Four different approaches for addressing the dyadic domain constraints are illustrated and critically compared.
The intensity-scale verification is applied to the case studies of the Intercomparison of Spatial Forecast Verification Methods Project. The geometric and synthetically perturbed cases show that the intensity-scale verification statistics are sensitive to displacement and bias errors. The intensity-scale skill score assesses the skill for different precipitation intensities and on different spatial scales, separately. The spatial scales of the error are attributed to both the size of the features and their displacement. The energy percentages allow one to objectively analyze the scale structure of the fields and to understand the intensity-scale relationship. Aggregated statistics for the Storm Prediction Center/National Severe Storms Laboratory (SPC/NSSL) 2005 Spring Program case studies show no significant differences among the models’ skill; however, the 4-km simulations of the NCEP version of the Weather Research and Forecast model (WRF4 NCEP) overforecast to a greater extent than the 2- and 4-km simulations of the NCAR version of the WRF (WRF2 and WRF4 NCAR). For the aggregated multiple cases, the different approaches addressing the dyadic domain constraints lead to similar results. On the other hand, for a single case, tiling provides the most robust and reliable approach, since it smoothes the effects of the discrete wavelet support and does not alter the original precipitation fields.
Abstract
A new scale decomposition of the Brier score for the verification of probabilistic forecasts defined on a spatial domain is introduced. The technique is illustrated on the Canadian Meteorological Centre (CMC) lightning probability forecasts. Probability forecasts of lightning occurrence in 3-h time windows and 24-km spatial resolution are verified against lightning observations from the North American Lightning Detection Network (NALDN) on a domain encompassing Canada and the northern United States. Verification is performed for lightning occurrences exceeding two different thresholds, to assess the forecast performance both for modest and intense lightning activity. Observation and forecast fields are decomposed into the sum of components on different spatial scales by performing a discrete 2D Haar wavelet decomposition. Wavelets, rather than Fourier transforms, were chosen because they are locally defined, and therefore more suitable for representing discontinuous spatial fields characterized by the presence of a few sparse nonzero values, such as lightning. Verification at different spatial scales is performed by evaluating Brier score and Brier skill score for each spatial-scale component. Reliability and resolution are also evaluated on different scales. Moreover, the bias on different scales is assessed, along with the ability of the forecasts to reproduce the observed-scale structure.
Abstract
A new scale decomposition of the Brier score for the verification of probabilistic forecasts defined on a spatial domain is introduced. The technique is illustrated on the Canadian Meteorological Centre (CMC) lightning probability forecasts. Probability forecasts of lightning occurrence in 3-h time windows and 24-km spatial resolution are verified against lightning observations from the North American Lightning Detection Network (NALDN) on a domain encompassing Canada and the northern United States. Verification is performed for lightning occurrences exceeding two different thresholds, to assess the forecast performance both for modest and intense lightning activity. Observation and forecast fields are decomposed into the sum of components on different spatial scales by performing a discrete 2D Haar wavelet decomposition. Wavelets, rather than Fourier transforms, were chosen because they are locally defined, and therefore more suitable for representing discontinuous spatial fields characterized by the presence of a few sparse nonzero values, such as lightning. Verification at different spatial scales is performed by evaluating Brier score and Brier skill score for each spatial-scale component. Reliability and resolution are also evaluated on different scales. Moreover, the bias on different scales is assessed, along with the ability of the forecasts to reproduce the observed-scale structure.
Abstract
Half-hourly GPS zenith tropospheric delay (ZTD) and collocated surface weather observations of pressure, temperature, and relative humidity are available in near–real time from the NOAA Global Systems Division (GSD) research GPS receiver network. These observations, located primarily over the continental United States, are assimilated in a research version of the Environment Canada (EC) regional (North America) analysis and forecast system. The impact of the assimilation on regional analyses and 0–48-h forecasts is evaluated for two periods: summer 2004 and winter 2004/05. Forecasts are verified against radiosonde, rain gauge, and NOAA GPS network observations.
The impacts of GPS ZTD and collocated surface weather observations for the summer period are generally positive, and include reductions in forecast errors for precipitable water, surface pressure, and geopotential height. It is shown that the ZTD data are primarily responsible for these forecast error reductions. The impact on precipitation forecasts is mixed, but more positive than negative, especially for the central U.S. region and for forecasts of larger precipitation amounts. Assimilation of the collocated surface weather data with ZTD contributes to the positive impact on precipitation forecasts for the central U.S. region. The NOAA GPS network data also have a positive impact on tropical storm system forecasts over the southeast United States, in terms of both storm track and precipitation. Impacts for the winter case are generally smaller because of the lower precipitable water (PW) over North America, but some positive impacts are observed for precipitation forecasts. The greatest regional impacts in the winter case are observed for the southeast U.S. (the Gulf) region where average PW is highest.
Abstract
Half-hourly GPS zenith tropospheric delay (ZTD) and collocated surface weather observations of pressure, temperature, and relative humidity are available in near–real time from the NOAA Global Systems Division (GSD) research GPS receiver network. These observations, located primarily over the continental United States, are assimilated in a research version of the Environment Canada (EC) regional (North America) analysis and forecast system. The impact of the assimilation on regional analyses and 0–48-h forecasts is evaluated for two periods: summer 2004 and winter 2004/05. Forecasts are verified against radiosonde, rain gauge, and NOAA GPS network observations.
The impacts of GPS ZTD and collocated surface weather observations for the summer period are generally positive, and include reductions in forecast errors for precipitable water, surface pressure, and geopotential height. It is shown that the ZTD data are primarily responsible for these forecast error reductions. The impact on precipitation forecasts is mixed, but more positive than negative, especially for the central U.S. region and for forecasts of larger precipitation amounts. Assimilation of the collocated surface weather data with ZTD contributes to the positive impact on precipitation forecasts for the central U.S. region. The NOAA GPS network data also have a positive impact on tropical storm system forecasts over the southeast United States, in terms of both storm track and precipitation. Impacts for the winter case are generally smaller because of the lower precipitable water (PW) over North America, but some positive impacts are observed for precipitation forecasts. The greatest regional impacts in the winter case are observed for the southeast U.S. (the Gulf) region where average PW is highest.
