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- Author or Editor: M.-D. Cheng x
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Abstract
Surface tracks of some cross-Taiwan tropical cyclones were discontinuous as a result of the blockage of the north-northeast–south-southwest-oriented Central Mountain Range (CMR). This paper tries to identify the variables that may be used to diagnose track continuity in advance. The track records of 131 westbound cross-Taiwan tropical cyclones between 1897 and 2009 are examined. It is found that the track continuity of a westbound cross-Taiwan tropical cyclone depends mostly upon the landfall location (YLF), the approaching direction (ANG), and the maximum wind (VMX) of the cyclone. According to the empirical probability of track continuity estimated from the data, the dependence on YLF, which is nonlinear and remarkably asymmetric with respect to the midpoint of the east coast, may be well approximated by a quadratic function of YLF. The nonlinearity and asymmetry can be interpreted in terms of the length scale of the CMR and the north–south antisymmetry of the cyclonic flow. The estimated dependence of track continuity on cyclone intensity and size may be approximated by a linear function of VMX. The estimated dependence of track continuity on ANG may be approximated by a single term of the modified variable DIR (=|ANG − 110|, where 110 is the direction, in degrees, perpendicular to the CMR’s long axis).
Using the 64 tracks between 1944 and 1996 as the training sample, a logistic regression equation model, built in terms of YLF, YLF square, DIR, and VMX gives an overall accuracy score of 89%. As to the probability estimates of individual tracks, 49 of the 64 tracks have estimated probabilities outside the (0.5 − 0.127, 0.5 + 0.127) RMS error range and are correctly classified. A prediction test using another set of 67 tracks not included in the model-training sample, scores a success rate of 82%. As to the probability predictions for individual tracks, 49 of the 67 tracks have predicted probabilities outside the RMS error range and are correctly predicted. These results confirm the appropriateness of the model and moreover demonstrate that the three parameters, YLF, DIR, and VMX, primarily control the surface track continuity of a westbound tropical cyclone crossing Taiwan.
Abstract
Surface tracks of some cross-Taiwan tropical cyclones were discontinuous as a result of the blockage of the north-northeast–south-southwest-oriented Central Mountain Range (CMR). This paper tries to identify the variables that may be used to diagnose track continuity in advance. The track records of 131 westbound cross-Taiwan tropical cyclones between 1897 and 2009 are examined. It is found that the track continuity of a westbound cross-Taiwan tropical cyclone depends mostly upon the landfall location (YLF), the approaching direction (ANG), and the maximum wind (VMX) of the cyclone. According to the empirical probability of track continuity estimated from the data, the dependence on YLF, which is nonlinear and remarkably asymmetric with respect to the midpoint of the east coast, may be well approximated by a quadratic function of YLF. The nonlinearity and asymmetry can be interpreted in terms of the length scale of the CMR and the north–south antisymmetry of the cyclonic flow. The estimated dependence of track continuity on cyclone intensity and size may be approximated by a linear function of VMX. The estimated dependence of track continuity on ANG may be approximated by a single term of the modified variable DIR (=|ANG − 110|, where 110 is the direction, in degrees, perpendicular to the CMR’s long axis).
Using the 64 tracks between 1944 and 1996 as the training sample, a logistic regression equation model, built in terms of YLF, YLF square, DIR, and VMX gives an overall accuracy score of 89%. As to the probability estimates of individual tracks, 49 of the 64 tracks have estimated probabilities outside the (0.5 − 0.127, 0.5 + 0.127) RMS error range and are correctly classified. A prediction test using another set of 67 tracks not included in the model-training sample, scores a success rate of 82%. As to the probability predictions for individual tracks, 49 of the 67 tracks have predicted probabilities outside the RMS error range and are correctly predicted. These results confirm the appropriateness of the model and moreover demonstrate that the three parameters, YLF, DIR, and VMX, primarily control the surface track continuity of a westbound tropical cyclone crossing Taiwan.
Abstract
The Indian Ocean Observing System (IndOOS), established in 2006, is a multinational network of sustained oceanic measurements that underpin understanding and forecasting of weather and climate for the Indian Ocean region and beyond. Almost one-third of humanity lives around the Indian Ocean, many in countries dependent on fisheries and rain-fed agriculture that are vulnerable to climate variability and extremes. The Indian Ocean alone has absorbed a quarter of the global oceanic heat uptake over the last two decades and the fate of this heat and its impact on future change is unknown. Climate models project accelerating sea level rise, more frequent extremes in monsoon rainfall, and decreasing oceanic productivity. In view of these new scientific challenges, a 3-yr international review of the IndOOS by more than 60 scientific experts now highlights the need for an enhanced observing network that can better meet societal challenges, and provide more reliable forecasts. Here we present core findings from this review, including the need for 1) chemical, biological, and ecosystem measurements alongside physical parameters; 2) expansion into the western tropics to improve understanding of the monsoon circulation; 3) better-resolved upper ocean processes to improve understanding of air–sea coupling and yield better subseasonal to seasonal predictions; and 4) expansion into key coastal regions and the deep ocean to better constrain the basinwide energy budget. These goals will require new agreements and partnerships with and among Indian Ocean rim countries, creating opportunities for them to enhance their monitoring and forecasting capacity as part of IndOOS-2.
Abstract
The Indian Ocean Observing System (IndOOS), established in 2006, is a multinational network of sustained oceanic measurements that underpin understanding and forecasting of weather and climate for the Indian Ocean region and beyond. Almost one-third of humanity lives around the Indian Ocean, many in countries dependent on fisheries and rain-fed agriculture that are vulnerable to climate variability and extremes. The Indian Ocean alone has absorbed a quarter of the global oceanic heat uptake over the last two decades and the fate of this heat and its impact on future change is unknown. Climate models project accelerating sea level rise, more frequent extremes in monsoon rainfall, and decreasing oceanic productivity. In view of these new scientific challenges, a 3-yr international review of the IndOOS by more than 60 scientific experts now highlights the need for an enhanced observing network that can better meet societal challenges, and provide more reliable forecasts. Here we present core findings from this review, including the need for 1) chemical, biological, and ecosystem measurements alongside physical parameters; 2) expansion into the western tropics to improve understanding of the monsoon circulation; 3) better-resolved upper ocean processes to improve understanding of air–sea coupling and yield better subseasonal to seasonal predictions; and 4) expansion into key coastal regions and the deep ocean to better constrain the basinwide energy budget. These goals will require new agreements and partnerships with and among Indian Ocean rim countries, creating opportunities for them to enhance their monitoring and forecasting capacity as part of IndOOS-2.
Drought-stricken areas of Central America and Mexico were victimized in 1998 by forest and brush fires that burned out of control during much of the first half of the year. Wind currents at various times during the episode helped transport smoke from these fires over the Gulf of Mexico and into portions of the United States. Visibilities were greatly reduced during favorable flow periods from New Mexico to south Florida and northward to Wisconsin as a result of this smoke and haze. In response to the reduced visibilities and increased pollutants, public health advisories and information statements were issued by various agencies in Gulf Coast states and in Oklahoma.
This event was also detected by a unique array of instrumentation deployed at the U.S. Department of Energy's Atmospheric Radiation Measurement (ARM) program Southern Great Plains Cloud and Radiation Testbed and by sensors of the Oklahoma Department of Environmental Quality/Air Quality Division. Observations from these measurement devices suggest elevated levels of aerosol loading and ozone concentrations during May 1998 when prevailing winds were favorable for the transport of the Central American smoke pall into Oklahoma and Kansas. In particular, aerosol extinction profiles derived from the ARM Raman lidar measurements revealed large variations in the vertical distribution of the smoke.
Drought-stricken areas of Central America and Mexico were victimized in 1998 by forest and brush fires that burned out of control during much of the first half of the year. Wind currents at various times during the episode helped transport smoke from these fires over the Gulf of Mexico and into portions of the United States. Visibilities were greatly reduced during favorable flow periods from New Mexico to south Florida and northward to Wisconsin as a result of this smoke and haze. In response to the reduced visibilities and increased pollutants, public health advisories and information statements were issued by various agencies in Gulf Coast states and in Oklahoma.
This event was also detected by a unique array of instrumentation deployed at the U.S. Department of Energy's Atmospheric Radiation Measurement (ARM) program Southern Great Plains Cloud and Radiation Testbed and by sensors of the Oklahoma Department of Environmental Quality/Air Quality Division. Observations from these measurement devices suggest elevated levels of aerosol loading and ozone concentrations during May 1998 when prevailing winds were favorable for the transport of the Central American smoke pall into Oklahoma and Kansas. In particular, aerosol extinction profiles derived from the ARM Raman lidar measurements revealed large variations in the vertical distribution of the smoke.
Abstract
Urbanization modifies atmospheric energy and moisture balances, forming distinct features [e.g., urban heat islands (UHIs) and enhanced or decreased precipitation]. These produce significant challenges to science and society, including rapid and intense flooding, heat waves strengthened by UHIs, and air pollutant haze. The Study of Urban Impacts on Rainfall and Fog/Haze (SURF) has brought together international expertise on observations and modeling, meteorology and atmospheric chemistry, and research and operational forecasting. The SURF overall science objective is a better understanding of urban, terrain, convection, and aerosol interactions for improved forecast accuracy. Specific objectives include a) promoting cooperative international research to improve understanding of urban summer convective precipitation and winter particulate episodes via extensive field studies, b) improving high-resolution urban weather and air quality forecast models, and c) enhancing urban weather forecasts for societal applications (e.g., health, energy, hydrologic, climate change, air quality, planning, and emergency response management). Preliminary SURF observational and modeling results are shown (i.e., turbulent PBL structure, bifurcating thunderstorms, haze events, urban canopy model development, and model forecast evaluation).
Abstract
Urbanization modifies atmospheric energy and moisture balances, forming distinct features [e.g., urban heat islands (UHIs) and enhanced or decreased precipitation]. These produce significant challenges to science and society, including rapid and intense flooding, heat waves strengthened by UHIs, and air pollutant haze. The Study of Urban Impacts on Rainfall and Fog/Haze (SURF) has brought together international expertise on observations and modeling, meteorology and atmospheric chemistry, and research and operational forecasting. The SURF overall science objective is a better understanding of urban, terrain, convection, and aerosol interactions for improved forecast accuracy. Specific objectives include a) promoting cooperative international research to improve understanding of urban summer convective precipitation and winter particulate episodes via extensive field studies, b) improving high-resolution urban weather and air quality forecast models, and c) enhancing urban weather forecasts for societal applications (e.g., health, energy, hydrologic, climate change, air quality, planning, and emergency response management). Preliminary SURF observational and modeling results are shown (i.e., turbulent PBL structure, bifurcating thunderstorms, haze events, urban canopy model development, and model forecast evaluation).
Abstract
The 12 weather and climate models participating in the Global Land–Atmosphere Coupling Experiment (GLACE) show both a wide variation in the strength of land–atmosphere coupling and some intriguing commonalities. In this paper, the causes of variations in coupling strength—both the geographic variations within a given model and the model-to-model differences—are addressed. The ability of soil moisture to affect precipitation is examined in two stages, namely, the ability of the soil moisture to affect evaporation, and the ability of evaporation to affect precipitation. Most of the differences between the models and within a given model are found to be associated with the first stage—an evaporation rate that varies strongly and consistently with soil moisture tends to lead to a higher coupling strength. The first-stage differences reflect identifiable differences in model parameterization and model climate. Intermodel differences in the evaporation–precipitation connection, however, also play a key role.
Abstract
The 12 weather and climate models participating in the Global Land–Atmosphere Coupling Experiment (GLACE) show both a wide variation in the strength of land–atmosphere coupling and some intriguing commonalities. In this paper, the causes of variations in coupling strength—both the geographic variations within a given model and the model-to-model differences—are addressed. The ability of soil moisture to affect precipitation is examined in two stages, namely, the ability of the soil moisture to affect evaporation, and the ability of evaporation to affect precipitation. Most of the differences between the models and within a given model are found to be associated with the first stage—an evaporation rate that varies strongly and consistently with soil moisture tends to lead to a higher coupling strength. The first-stage differences reflect identifiable differences in model parameterization and model climate. Intermodel differences in the evaporation–precipitation connection, however, also play a key role.
Abstract
The Global Land–Atmosphere Coupling Experiment (GLACE) is a model intercomparison study focusing on a typically neglected yet critical element of numerical weather and climate modeling: land–atmosphere coupling strength, or the degree to which anomalies in land surface state (e.g., soil moisture) can affect rainfall generation and other atmospheric processes. The 12 AGCM groups participating in GLACE performed a series of simple numerical experiments that allow the objective quantification of this element for boreal summer. The derived coupling strengths vary widely. Some similarity, however, is found in the spatial patterns generated by the models, with enough similarity to pinpoint multimodel “hot spots” of land–atmosphere coupling. For boreal summer, such hot spots for precipitation and temperature are found over large regions of Africa, central North America, and India; a hot spot for temperature is also found over eastern China. The design of the GLACE simulations are described in full detail so that any interested modeling group can repeat them easily and thereby place their model’s coupling strength within the broad range of those documented here.
Abstract
The Global Land–Atmosphere Coupling Experiment (GLACE) is a model intercomparison study focusing on a typically neglected yet critical element of numerical weather and climate modeling: land–atmosphere coupling strength, or the degree to which anomalies in land surface state (e.g., soil moisture) can affect rainfall generation and other atmospheric processes. The 12 AGCM groups participating in GLACE performed a series of simple numerical experiments that allow the objective quantification of this element for boreal summer. The derived coupling strengths vary widely. Some similarity, however, is found in the spatial patterns generated by the models, with enough similarity to pinpoint multimodel “hot spots” of land–atmosphere coupling. For boreal summer, such hot spots for precipitation and temperature are found over large regions of Africa, central North America, and India; a hot spot for temperature is also found over eastern China. The design of the GLACE simulations are described in full detail so that any interested modeling group can repeat them easily and thereby place their model’s coupling strength within the broad range of those documented here.
Abstract
Precipitation sustains life and supports human activities, making its prediction one of the most societally relevant challenges in weather and climate modeling. Limitations in modeling precipitation underscore the need for diagnostics and metrics to evaluate precipitation in simulations and predictions. While routine use of basic metrics is important for documenting model skill, more sophisticated diagnostics and metrics aimed at connecting model biases to their sources and revealing precipitation characteristics relevant to how model precipitation is used are critical for improving models and their uses. This paper illustrates examples of exploratory diagnostics and metrics including 1) spatiotemporal characteristics metrics such as diurnal variability, probability of extremes, duration of dry spells, spectral characteristics, and spatiotemporal coherence of precipitation; 2) process-oriented metrics based on the rainfall–moisture coupling and temperature–water vapor environments of precipitation; and 3) phenomena-based metrics focusing on precipitation associated with weather phenomena including low pressure systems, mesoscale convective systems, frontal systems, and atmospheric rivers. Together, these diagnostics and metrics delineate the multifaceted and multiscale nature of precipitation, its relations with the environments, and its generation mechanisms. The metrics are applied to historical simulations from phases 5 and 6 of the Coupled Model Intercomparison Project. Models exhibit diverse skill as measured by the suite of metrics, with very few models consistently ranked as top or bottom performers compared to other models in multiple metrics. Analysis of model skill across metrics and models suggests possible relationships among subsets of metrics, motivating the need for more systematic analysis to understand model biases for informing model development.
Abstract
Precipitation sustains life and supports human activities, making its prediction one of the most societally relevant challenges in weather and climate modeling. Limitations in modeling precipitation underscore the need for diagnostics and metrics to evaluate precipitation in simulations and predictions. While routine use of basic metrics is important for documenting model skill, more sophisticated diagnostics and metrics aimed at connecting model biases to their sources and revealing precipitation characteristics relevant to how model precipitation is used are critical for improving models and their uses. This paper illustrates examples of exploratory diagnostics and metrics including 1) spatiotemporal characteristics metrics such as diurnal variability, probability of extremes, duration of dry spells, spectral characteristics, and spatiotemporal coherence of precipitation; 2) process-oriented metrics based on the rainfall–moisture coupling and temperature–water vapor environments of precipitation; and 3) phenomena-based metrics focusing on precipitation associated with weather phenomena including low pressure systems, mesoscale convective systems, frontal systems, and atmospheric rivers. Together, these diagnostics and metrics delineate the multifaceted and multiscale nature of precipitation, its relations with the environments, and its generation mechanisms. The metrics are applied to historical simulations from phases 5 and 6 of the Coupled Model Intercomparison Project. Models exhibit diverse skill as measured by the suite of metrics, with very few models consistently ranked as top or bottom performers compared to other models in multiple metrics. Analysis of model skill across metrics and models suggests possible relationships among subsets of metrics, motivating the need for more systematic analysis to understand model biases for informing model development.
Abstract
A full description of the ModelE version of the Goddard Institute for Space Studies (GISS) atmospheric general circulation model (GCM) and results are presented for present-day climate simulations (ca. 1979). This version is a complete rewrite of previous models incorporating numerous improvements in basic physics, the stratospheric circulation, and forcing fields. Notable changes include the following: the model top is now above the stratopause, the number of vertical layers has increased, a new cloud microphysical scheme is used, vegetation biophysics now incorporates a sensitivity to humidity, atmospheric turbulence is calculated over the whole column, and new land snow and lake schemes are introduced. The performance of the model using three configurations with different horizontal and vertical resolutions is compared to quality-controlled in situ data, remotely sensed and reanalysis products. Overall, significant improvements over previous models are seen, particularly in upper-atmosphere temperatures and winds, cloud heights, precipitation, and sea level pressure. Data–model comparisons continue, however, to highlight persistent problems in the marine stratocumulus regions.
Abstract
A full description of the ModelE version of the Goddard Institute for Space Studies (GISS) atmospheric general circulation model (GCM) and results are presented for present-day climate simulations (ca. 1979). This version is a complete rewrite of previous models incorporating numerous improvements in basic physics, the stratospheric circulation, and forcing fields. Notable changes include the following: the model top is now above the stratopause, the number of vertical layers has increased, a new cloud microphysical scheme is used, vegetation biophysics now incorporates a sensitivity to humidity, atmospheric turbulence is calculated over the whole column, and new land snow and lake schemes are introduced. The performance of the model using three configurations with different horizontal and vertical resolutions is compared to quality-controlled in situ data, remotely sensed and reanalysis products. Overall, significant improvements over previous models are seen, particularly in upper-atmosphere temperatures and winds, cloud heights, precipitation, and sea level pressure. Data–model comparisons continue, however, to highlight persistent problems in the marine stratocumulus regions.
