Search Results
Abstract
A precipitation climatology has been developed for the relative frequencies of zero, one, or two or more days with measurable precipitation within 5-day periods. In addition, the distribution of precipitation amounts is given for the one wet day in five and for the more than one wet day in five categories. The purpose of the climatology is to provide background for the development and introduction of extended-range (6–10 day forecast period) precipitation forecasts in terms of the probabilities of the three categories.
The climatology is based on 36 years of precipitation data at 146 stations in the contiguous United States. Details of the treatment of the data are provided. Diagrams are developed to display the seasonal patterns of frequency and amount for individual stations. The frequency diagram is a nomogram based on a simple Markov chain model for precipitation occurrences. It can be used to infer—from the frequencies of 0 and exactly 1 wet day in 5—single-day climatological precipitation probabilities or the probabilities conditional on precipitation falling on the previous day (or to infer—from the daily climatology and knowledge of the persistence—the probability of the three categories of the 5-day period). These diagrams are useful (as will be demonstrated by example) for describing and comparing precipitation climatologies. They should also aid the forecaster in making and interpreting probability forecasts of precipitation frequency for the 6–10 day period, where day-by-day forecasts are unfeasible.
Abstract
A precipitation climatology has been developed for the relative frequencies of zero, one, or two or more days with measurable precipitation within 5-day periods. In addition, the distribution of precipitation amounts is given for the one wet day in five and for the more than one wet day in five categories. The purpose of the climatology is to provide background for the development and introduction of extended-range (6–10 day forecast period) precipitation forecasts in terms of the probabilities of the three categories.
The climatology is based on 36 years of precipitation data at 146 stations in the contiguous United States. Details of the treatment of the data are provided. Diagrams are developed to display the seasonal patterns of frequency and amount for individual stations. The frequency diagram is a nomogram based on a simple Markov chain model for precipitation occurrences. It can be used to infer—from the frequencies of 0 and exactly 1 wet day in 5—single-day climatological precipitation probabilities or the probabilities conditional on precipitation falling on the previous day (or to infer—from the daily climatology and knowledge of the persistence—the probability of the three categories of the 5-day period). These diagrams are useful (as will be demonstrated by example) for describing and comparing precipitation climatologies. They should also aid the forecaster in making and interpreting probability forecasts of precipitation frequency for the 6–10 day period, where day-by-day forecasts are unfeasible.
Abstract
Six recurrent thermal regimes are identified over continental North America from June to September through a k-means clustering applied to daily maximum temperature simulated by ECHAM5 forced by historical SSTs for 1930–2013 and validated using NCEP–DOE AMIP-II reanalysis over the 1980–2009 period. Four regimes are related to a synoptic wave pattern propagating eastward in the midlatitudes with embedded ridging anomalies that translate into maximum warming transiting along. Two other regimes, associated with broad continental warming and above average temperatures in the northeastern United States, respectively, are characterized by ridging anomalies over North America, Europe, and Asia that suggest correlated heat wave occurrences in these regions. Their frequencies are mainly related to both La Niña and warm conditions in the North Atlantic. Removing all variability beyond the seasonal cycle in the North Atlantic in ECHAM5 leads to a significant drop in the occurrences of the regime associated with warming in the northeastern United States. Superimposing positive (negative) anomalies mimicking the Atlantic multidecadal variability (AMV) in the North Atlantic translates into more (less) warming over the United States across all regimes, and does alter regime frequencies but less significantly. Regime frequency changes are thus primarily controlled by Atlantic SST variability on all time scales beyond the seasonal cycle, rather than mean SST changes, whereas the intensity of temperature anomalies is impacted by AMV SST forcing, because of upper-tropospheric warming and enhanced stability suppressing rising motion during the positive phase of the AMV.
Abstract
Six recurrent thermal regimes are identified over continental North America from June to September through a k-means clustering applied to daily maximum temperature simulated by ECHAM5 forced by historical SSTs for 1930–2013 and validated using NCEP–DOE AMIP-II reanalysis over the 1980–2009 period. Four regimes are related to a synoptic wave pattern propagating eastward in the midlatitudes with embedded ridging anomalies that translate into maximum warming transiting along. Two other regimes, associated with broad continental warming and above average temperatures in the northeastern United States, respectively, are characterized by ridging anomalies over North America, Europe, and Asia that suggest correlated heat wave occurrences in these regions. Their frequencies are mainly related to both La Niña and warm conditions in the North Atlantic. Removing all variability beyond the seasonal cycle in the North Atlantic in ECHAM5 leads to a significant drop in the occurrences of the regime associated with warming in the northeastern United States. Superimposing positive (negative) anomalies mimicking the Atlantic multidecadal variability (AMV) in the North Atlantic translates into more (less) warming over the United States across all regimes, and does alter regime frequencies but less significantly. Regime frequency changes are thus primarily controlled by Atlantic SST variability on all time scales beyond the seasonal cycle, rather than mean SST changes, whereas the intensity of temperature anomalies is impacted by AMV SST forcing, because of upper-tropospheric warming and enhanced stability suppressing rising motion during the positive phase of the AMV.
