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Abstract
A method of improving the accuracy of model output statistics (MOS) probability of precipitation (POP) forecasts was investigated. The method uses a perfect prog (PP) forecast as a potential predictor in a MOS equation. The PP method, with its larger developmental databases has the potential of incorporating additional information about local climatology, seasonality, and synoptic pattern type, which might be otherwise lacking in the MOS predictor dataset.
Three PP models were developed: an analog model, a 1ogistic regression model and an analog/regression hybrid model. The POP forecasts were generated by the three PP models and the MOS model at four Pennsylvania stations by using 6 months of independent limited-area fine mesh (LFM) forecasts. Three MOS/PP combination models were derived by linearly combining MOS with each of the three PP models. The MOS/PP combination model forecasts were generated with the independent MOS and PP forecasts by using a cross-validation technique. The three MOS/PP combination models showed a small improvement over the MOS model. The probability that thew improvements were from random chance ranged from 6% to 33%.
Abstract
A method of improving the accuracy of model output statistics (MOS) probability of precipitation (POP) forecasts was investigated. The method uses a perfect prog (PP) forecast as a potential predictor in a MOS equation. The PP method, with its larger developmental databases has the potential of incorporating additional information about local climatology, seasonality, and synoptic pattern type, which might be otherwise lacking in the MOS predictor dataset.
Three PP models were developed: an analog model, a 1ogistic regression model and an analog/regression hybrid model. The POP forecasts were generated by the three PP models and the MOS model at four Pennsylvania stations by using 6 months of independent limited-area fine mesh (LFM) forecasts. Three MOS/PP combination models were derived by linearly combining MOS with each of the three PP models. The MOS/PP combination model forecasts were generated with the independent MOS and PP forecasts by using a cross-validation technique. The three MOS/PP combination models showed a small improvement over the MOS model. The probability that thew improvements were from random chance ranged from 6% to 33%.
Abstract
The one-dimensional terrain height variance spectra of the Front Range west of Boulder, Colorado, is compared with that of the hypothetical two-dimensional mountain used by several investigators in their modeling studies of downslope winds. The terrain height variance of the hypothetical mountain exceeds that of the Front Range on scales longer than 25 km. On shorter scales, the terrain height variance of the hypothetical mountain decreases at an unrealistic rate.
The scales at which terrain features of the Front Range can be considered approximately two-dimensional are determined. On scales longer than 25 km, the Front Range is essentially two-dimensional. However, on shorter scales, the Front Range exhibits equal variance in the along-range and cross-range directions.
Abstract
The one-dimensional terrain height variance spectra of the Front Range west of Boulder, Colorado, is compared with that of the hypothetical two-dimensional mountain used by several investigators in their modeling studies of downslope winds. The terrain height variance of the hypothetical mountain exceeds that of the Front Range on scales longer than 25 km. On shorter scales, the terrain height variance of the hypothetical mountain decreases at an unrealistic rate.
The scales at which terrain features of the Front Range can be considered approximately two-dimensional are determined. On scales longer than 25 km, the Front Range is essentially two-dimensional. However, on shorter scales, the Front Range exhibits equal variance in the along-range and cross-range directions.
Abstract
Bursts in the kinematic vertical transports of heat and horizontal momentum in a moderately convective marine atmospheric surface layer are studied by applying the variable interval time averaging (VITA) detection method to principal components analysis (PCA)–decomposed datasets obtained from the Floating Instrumentation Platform (FLIP) moored vessel during the 1995 April–May Pacific Marine Boundary Layer (PMBL) experiment. For convective plumes, a well-defined dimensionless relationship is shown to exist between the vertical transports of heat and horizontal momentum; this relationship cannot be easily deduced if PCA and VITA are not both applied.
PCA decomposes a dataset using correlations within that dataset instead of bandpass filtering it to retain energy in a predetermined range of scales; PCA thus respects all scales contributing to the phenomena retained in the dataset. Subsequent use of cross-spectral techniques to group the PCA-decomposed dataset into coherent structure types leads to, among other types of coherent structures, PCA-derived plumes. The VITA method is applied to a decomposed dataset in order to identify updrafts (bursts) and downdrafts (sweeps) in the time series of correlated variables by searching the signal for events that satisfy user-specified criteria. With proper use of PCA, surface-layer plumes can be reassembled in a way that yields the same transport relationships no matter which of the two different detecting variables is used.
Abstract
Bursts in the kinematic vertical transports of heat and horizontal momentum in a moderately convective marine atmospheric surface layer are studied by applying the variable interval time averaging (VITA) detection method to principal components analysis (PCA)–decomposed datasets obtained from the Floating Instrumentation Platform (FLIP) moored vessel during the 1995 April–May Pacific Marine Boundary Layer (PMBL) experiment. For convective plumes, a well-defined dimensionless relationship is shown to exist between the vertical transports of heat and horizontal momentum; this relationship cannot be easily deduced if PCA and VITA are not both applied.
PCA decomposes a dataset using correlations within that dataset instead of bandpass filtering it to retain energy in a predetermined range of scales; PCA thus respects all scales contributing to the phenomena retained in the dataset. Subsequent use of cross-spectral techniques to group the PCA-decomposed dataset into coherent structure types leads to, among other types of coherent structures, PCA-derived plumes. The VITA method is applied to a decomposed dataset in order to identify updrafts (bursts) and downdrafts (sweeps) in the time series of correlated variables by searching the signal for events that satisfy user-specified criteria. With proper use of PCA, surface-layer plumes can be reassembled in a way that yields the same transport relationships no matter which of the two different detecting variables is used.
Abstract
This study presents an overview of the El Niño–Southern Oscillation (ENSO) phenomenon and Pacific decadal variability (PDV) simulated in a multicentury preindustrial control integration of the NCAR Community Climate System Model version 4 (CCSM4) at nominal 1° latitude–longitude resolution. Several aspects of ENSO are improved in CCSM4 compared to its predecessor CCSM3, including the lengthened period (3–6 yr), the larger range of amplitude and frequency of events, and the longer duration of La Niña compared to El Niño. However, the overall magnitude of ENSO in CCSM4 is overestimated by ~30%. The simulated ENSO exhibits characteristics consistent with the delayed/recharge oscillator paradigm, including correspondence between the lengthened period and increased latitudinal width of the anomalous equatorial zonal wind stress. Global seasonal atmospheric teleconnections with accompanying impacts on precipitation and temperature are generally well simulated, although the wintertime deepening of the Aleutian low erroneously persists into spring. The vertical structure of the upper-ocean temperature response to ENSO in the north and south Pacific displays a realistic seasonal evolution, with notable asymmetries between warm and cold events. The model shows evidence of atmospheric circulation precursors over the North Pacific associated with the “seasonal footprinting mechanism,” similar to observations. Simulated PDV exhibits a significant spectral peak around 15 yr, with generally realistic spatial pattern and magnitude. However, PDV linkages between the tropics and extratropics are weaker than observed.
Abstract
This study presents an overview of the El Niño–Southern Oscillation (ENSO) phenomenon and Pacific decadal variability (PDV) simulated in a multicentury preindustrial control integration of the NCAR Community Climate System Model version 4 (CCSM4) at nominal 1° latitude–longitude resolution. Several aspects of ENSO are improved in CCSM4 compared to its predecessor CCSM3, including the lengthened period (3–6 yr), the larger range of amplitude and frequency of events, and the longer duration of La Niña compared to El Niño. However, the overall magnitude of ENSO in CCSM4 is overestimated by ~30%. The simulated ENSO exhibits characteristics consistent with the delayed/recharge oscillator paradigm, including correspondence between the lengthened period and increased latitudinal width of the anomalous equatorial zonal wind stress. Global seasonal atmospheric teleconnections with accompanying impacts on precipitation and temperature are generally well simulated, although the wintertime deepening of the Aleutian low erroneously persists into spring. The vertical structure of the upper-ocean temperature response to ENSO in the north and south Pacific displays a realistic seasonal evolution, with notable asymmetries between warm and cold events. The model shows evidence of atmospheric circulation precursors over the North Pacific associated with the “seasonal footprinting mechanism,” similar to observations. Simulated PDV exhibits a significant spectral peak around 15 yr, with generally realistic spatial pattern and magnitude. However, PDV linkages between the tropics and extratropics are weaker than observed.
During the summer of 1989, the Forecast Systems Laboratory of the National Oceanic and Atmospheric Administration sponsored an evaluation of artificial-intelligence-based systems that forecast severe convective storms. The evaluation experiment, called Shootout-89, took place in Boulder, Colorado, and focused on storms over the northeastern Colorado foothills and plains.
Six systems participated in Shootout-89: three traditional expert systems, a hybrid system including a linear model augmented by a small expert system, an analogue-based system, and a system developed using methods from the cognitive science/judgment analysis tradition.
Each day of the exercise, the systems generated 2–9-h forecasts of the probabilities of occurrence of nonsignificant weather, significant weather, and severe weather in each of four regions in northeastern Colorado. A verification coordinator working at the Denver Weather Service Forecast Office gathered ground-truth data from a network of observers.
The systems were evaluated on several measures of forecast skill, on timeliness, on ease of learning, and on ease of use. They were generally easy to operate; however, they required substantially different levels of meteorological expertise on the part of their users, reflecting the various operational environments for which they had been designed. The systems varied in their statistical behavior, but on this difficult forecast problem, they generally showed a skill approximately equal to that of persistence forecasts and climatological forecasts.
During the summer of 1989, the Forecast Systems Laboratory of the National Oceanic and Atmospheric Administration sponsored an evaluation of artificial-intelligence-based systems that forecast severe convective storms. The evaluation experiment, called Shootout-89, took place in Boulder, Colorado, and focused on storms over the northeastern Colorado foothills and plains.
Six systems participated in Shootout-89: three traditional expert systems, a hybrid system including a linear model augmented by a small expert system, an analogue-based system, and a system developed using methods from the cognitive science/judgment analysis tradition.
Each day of the exercise, the systems generated 2–9-h forecasts of the probabilities of occurrence of nonsignificant weather, significant weather, and severe weather in each of four regions in northeastern Colorado. A verification coordinator working at the Denver Weather Service Forecast Office gathered ground-truth data from a network of observers.
The systems were evaluated on several measures of forecast skill, on timeliness, on ease of learning, and on ease of use. They were generally easy to operate; however, they required substantially different levels of meteorological expertise on the part of their users, reflecting the various operational environments for which they had been designed. The systems varied in their statistical behavior, but on this difficult forecast problem, they generally showed a skill approximately equal to that of persistence forecasts and climatological forecasts.
This scientific assessment examines changes in three climate extremes—extratropical storms, winds, and waves—with an emphasis on U.S. coastal regions during the cold season. There is moderate evidence of an increase in both extratropical storm frequency and intensity during the cold season in the Northern Hemisphere since 1950, with suggestive evidence of geographic shifts resulting in slight upward trends in offshore/coastal regions. There is also suggestive evidence of an increase in extreme winds (at least annually) over parts of the ocean since the early to mid-1980s, but the evidence over the U.S. land surface is inconclusive. Finally, there is moderate evidence of an increase in extreme waves in winter along the Pacific coast since the 1950s, but along other U.S. shorelines any tendencies are of modest magnitude compared with historical variability. The data for extratropical cyclones are considered to be of relatively high quality for trend detection, whereas the data for extreme winds and waves are judged to be of intermediate quality. In terms of physical causes leading to multidecadal changes, the level of understanding for both extratropical storms and extreme winds is considered to be relatively low, while that for extreme waves is judged to be intermediate. Since the ability to measure these changes with some confidence is relatively recent, understanding is expected to improve in the future for a variety of reasons, including increased periods of record and the development of “climate reanalysis” projects.
This scientific assessment examines changes in three climate extremes—extratropical storms, winds, and waves—with an emphasis on U.S. coastal regions during the cold season. There is moderate evidence of an increase in both extratropical storm frequency and intensity during the cold season in the Northern Hemisphere since 1950, with suggestive evidence of geographic shifts resulting in slight upward trends in offshore/coastal regions. There is also suggestive evidence of an increase in extreme winds (at least annually) over parts of the ocean since the early to mid-1980s, but the evidence over the U.S. land surface is inconclusive. Finally, there is moderate evidence of an increase in extreme waves in winter along the Pacific coast since the 1950s, but along other U.S. shorelines any tendencies are of modest magnitude compared with historical variability. The data for extratropical cyclones are considered to be of relatively high quality for trend detection, whereas the data for extreme winds and waves are judged to be of intermediate quality. In terms of physical causes leading to multidecadal changes, the level of understanding for both extratropical storms and extreme winds is considered to be relatively low, while that for extreme waves is judged to be intermediate. Since the ability to measure these changes with some confidence is relatively recent, understanding is expected to improve in the future for a variety of reasons, including increased periods of record and the development of “climate reanalysis” projects.
A severe 5-day lake-effect storm resulted in eight deaths, hundreds of injuries, and over $3 million in damage to a small area of northeastern Ohio and northwestern Pennsylvania in November 1996. In 1999, a blizzard associated with an intense cyclone disabled Chicago and much of the U.S. Midwest with 30–90 cm of snow. Such winter weather conditions have many impacts on the lives and property of people throughout much of North America. Each of these events is the culmination of a complex interaction between synoptic-scale, mesoscale, and microscale processes.
An understanding of how the multiple size scales and timescales interact is critical to improving forecasting of these severe winter weather events. The Lake-Induced Convection Experiment (Lake-ICE) and the Snowband Dynamics Project (SNOWBAND) collected comprehensive datasets on processes involved in lake-effect snowstorms and snowbands associated with cyclones during the winter of 1997/98. This paper outlines the goals and operations of these collaborative projects. Preliminary findings are given with illustrative examples of new state-of-the-art research observations collected. Analyses associated with Lake-ICE and SNOWBAND hold the promise of greatly improving our scientific understanding of processes involved in these important wintertime phenomena.
A severe 5-day lake-effect storm resulted in eight deaths, hundreds of injuries, and over $3 million in damage to a small area of northeastern Ohio and northwestern Pennsylvania in November 1996. In 1999, a blizzard associated with an intense cyclone disabled Chicago and much of the U.S. Midwest with 30–90 cm of snow. Such winter weather conditions have many impacts on the lives and property of people throughout much of North America. Each of these events is the culmination of a complex interaction between synoptic-scale, mesoscale, and microscale processes.
An understanding of how the multiple size scales and timescales interact is critical to improving forecasting of these severe winter weather events. The Lake-Induced Convection Experiment (Lake-ICE) and the Snowband Dynamics Project (SNOWBAND) collected comprehensive datasets on processes involved in lake-effect snowstorms and snowbands associated with cyclones during the winter of 1997/98. This paper outlines the goals and operations of these collaborative projects. Preliminary findings are given with illustrative examples of new state-of-the-art research observations collected. Analyses associated with Lake-ICE and SNOWBAND hold the promise of greatly improving our scientific understanding of processes involved in these important wintertime phenomena.
The Climate Absolute Radiance and Refractivity Observatory (CLARREO) mission will provide a calibration laboratory in orbit for the purpose of accurately measuring and attributing climate change. CLARREO measurements establish new climate change benchmarks with high absolute radiometric accuracy and high statistical confidence across a wide range of essential climate variables. CLARREO's inherently high absolute accuracy will be verified and traceable on orbit to Système Internationale (SI) units. The benchmarks established by CLARREO will be critical for assessing changes in the Earth system and climate model predictive capabilities for decades into the future as society works to meet the challenge of optimizing strategies for mitigating and adapting to climate change. The CLARREO benchmarks are derived from measurements of the Earth's thermal infrared spectrum (5–50 μm), the spectrum of solar radiation reflected by the Earth and its atmosphere (320–2300 nm), and radio occultation refractivity from which accurate temperature profiles are derived. The mission has the ability to provide new spectral fingerprints of climate change, as well as to provide the first orbiting radiometer with accuracy sufficient to serve as the reference transfer standard for other space sensors, in essence serving as a “NIST [National Institute of Standards and Technology] in orbit.” CLARREO will greatly improve the accuracy and relevance of a wide range of space-borne instruments for decadal climate change. Finally, CLARREO has developed new metrics and methods for determining the accuracy requirements of climate observations for a wide range of climate variables and uncertainty sources. These methods should be useful for improving our understanding of observing requirements for most climate change observations.
The Climate Absolute Radiance and Refractivity Observatory (CLARREO) mission will provide a calibration laboratory in orbit for the purpose of accurately measuring and attributing climate change. CLARREO measurements establish new climate change benchmarks with high absolute radiometric accuracy and high statistical confidence across a wide range of essential climate variables. CLARREO's inherently high absolute accuracy will be verified and traceable on orbit to Système Internationale (SI) units. The benchmarks established by CLARREO will be critical for assessing changes in the Earth system and climate model predictive capabilities for decades into the future as society works to meet the challenge of optimizing strategies for mitigating and adapting to climate change. The CLARREO benchmarks are derived from measurements of the Earth's thermal infrared spectrum (5–50 μm), the spectrum of solar radiation reflected by the Earth and its atmosphere (320–2300 nm), and radio occultation refractivity from which accurate temperature profiles are derived. The mission has the ability to provide new spectral fingerprints of climate change, as well as to provide the first orbiting radiometer with accuracy sufficient to serve as the reference transfer standard for other space sensors, in essence serving as a “NIST [National Institute of Standards and Technology] in orbit.” CLARREO will greatly improve the accuracy and relevance of a wide range of space-borne instruments for decadal climate change. Finally, CLARREO has developed new metrics and methods for determining the accuracy requirements of climate observations for a wide range of climate variables and uncertainty sources. These methods should be useful for improving our understanding of observing requirements for most climate change observations.
