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- Author or Editor: Qin Zhang x
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Abstract
The anomalous Philippine Sea anticyclone (PSAC) conveys impacts of El Niño to east Asian climate during the mature and decay of an El Niño (from the winter to ensuing summer). It is shown that the anomalous PSAC forms in fall about one season prior to the peak El Niño; its strength increases with the El Niño intensity and its sign reverses during a La Niña. The PSAC formation concurs with abnormal deepening of the east Asian trough and with increasing number of northward recurvature of tropical storms in the western Pacific. The PSAC establishment is abrupt, coupling with a swing from a wet to dry phase of an intraseasonal oscillation (ISO) and often concurrent with early retreat of the east Asian summer monsoon. The ISO becomes inactive after PSAC establishment.
The development of the PSAC is attributed to combined effects of the remote El Niño forcing, tropical–extratropical interaction, and monsoon–ocean interaction. The developing El Niño induces off-equatorial ascending Rossby wave responses and land surface cooling in northeast Asia; both deepen the east Asian trough in fall and induces vigorous tropical–extratropical exchange of air mass and heat, which enhances the cold air outbreak and initiation of the PSAC. Through exciting descending Rossby waves, the El Niño–induced Indonesian subsidence generates low-level anticyclonic vorticity over south Asia, which is advected by mean monsoon westerly, instigating the anomalous PSAC. The ISO interacting with the underlying ocean plays a critical role in the abrupt establishment of PSAC. The wind–evaporation/entrainment feedback tends to amplify (suppress) ISO before (after) winter northeasterly monsoon commences, suggesting the roles of atmosphere–ocean interaction and the seasonal march of background winds in changing the Philippine Sea ISO intensity and maintaining PSAC.
Abstract
The anomalous Philippine Sea anticyclone (PSAC) conveys impacts of El Niño to east Asian climate during the mature and decay of an El Niño (from the winter to ensuing summer). It is shown that the anomalous PSAC forms in fall about one season prior to the peak El Niño; its strength increases with the El Niño intensity and its sign reverses during a La Niña. The PSAC formation concurs with abnormal deepening of the east Asian trough and with increasing number of northward recurvature of tropical storms in the western Pacific. The PSAC establishment is abrupt, coupling with a swing from a wet to dry phase of an intraseasonal oscillation (ISO) and often concurrent with early retreat of the east Asian summer monsoon. The ISO becomes inactive after PSAC establishment.
The development of the PSAC is attributed to combined effects of the remote El Niño forcing, tropical–extratropical interaction, and monsoon–ocean interaction. The developing El Niño induces off-equatorial ascending Rossby wave responses and land surface cooling in northeast Asia; both deepen the east Asian trough in fall and induces vigorous tropical–extratropical exchange of air mass and heat, which enhances the cold air outbreak and initiation of the PSAC. Through exciting descending Rossby waves, the El Niño–induced Indonesian subsidence generates low-level anticyclonic vorticity over south Asia, which is advected by mean monsoon westerly, instigating the anomalous PSAC. The ISO interacting with the underlying ocean plays a critical role in the abrupt establishment of PSAC. The wind–evaporation/entrainment feedback tends to amplify (suppress) ISO before (after) winter northeasterly monsoon commences, suggesting the roles of atmosphere–ocean interaction and the seasonal march of background winds in changing the Philippine Sea ISO intensity and maintaining PSAC.
Abstract
A comprehensive field measurement was set up in the springtime of 1993 to study the chemical characteristics of precipitation in the Lushan Mountains. The results showed that the concentrations of gaseous SO2, NO x , NH3, and HNO3 were low and precipitation was seriously acidified. The pH of all samples was less than 5.6 with an average of 4.6 and a minimum of 3.71. Sulfuric acid was the primary acidic substance to cause the acidification and nitric acid was the secondary. Both acidity and concentrations of compositions in cloud water were much higher than those in rainwater. Comparisons of different phases of precipitation showed that concentrations of ionic components in the solid phase were two to five times higher than those in rainwater, and its pH was 0.6 higher than that of rainwater. According to the measurement results, it was postulated that the acidification of precipitation was caused by joint effects of washout of local atmospheric pollutants and long-distance transportation of acidic substances.
Abstract
A comprehensive field measurement was set up in the springtime of 1993 to study the chemical characteristics of precipitation in the Lushan Mountains. The results showed that the concentrations of gaseous SO2, NO x , NH3, and HNO3 were low and precipitation was seriously acidified. The pH of all samples was less than 5.6 with an average of 4.6 and a minimum of 3.71. Sulfuric acid was the primary acidic substance to cause the acidification and nitric acid was the secondary. Both acidity and concentrations of compositions in cloud water were much higher than those in rainwater. Comparisons of different phases of precipitation showed that concentrations of ionic components in the solid phase were two to five times higher than those in rainwater, and its pH was 0.6 higher than that of rainwater. According to the measurement results, it was postulated that the acidification of precipitation was caused by joint effects of washout of local atmospheric pollutants and long-distance transportation of acidic substances.
Abstract
Radar echoes from migrating birds can severely contaminate Doppler velocity measurements. For meteorological applications, especially quantitative applications in radar data assimilation, it is necessary to remove bird-contaminated velocity scans by using an automated identification technique. Such a technique should be also useful for ornithologists in selecting bird echoes automatically from radar scans. This technique can be developed in two steps: (i) extract the main features of migrating-bird echoes from reflectivity and Doppler velocity images and find proper parameters to quantify these features; (ii) utilize these parameters to develop an automated quality control procedure to identify and flag migrating-bird-contaminated Doppler velocity scans (sweeps). The first step is accomplished in this study (Part I) by identifying possible migrating-bird echoes in the level II data collected from the Oklahoma KTLX radar during the 2003 spring migrating season. The identifications are further verified by polarimetric radar measurements from the National Severe Storms Laboratory (NSSL) KOUN radar, Geostationary Operational Environmental Satellite (GOES) IR images, and rawinsonde measurements. Three proper parameters are found, and their histograms are prepared for the second step of development (reported in Part II).
Abstract
Radar echoes from migrating birds can severely contaminate Doppler velocity measurements. For meteorological applications, especially quantitative applications in radar data assimilation, it is necessary to remove bird-contaminated velocity scans by using an automated identification technique. Such a technique should be also useful for ornithologists in selecting bird echoes automatically from radar scans. This technique can be developed in two steps: (i) extract the main features of migrating-bird echoes from reflectivity and Doppler velocity images and find proper parameters to quantify these features; (ii) utilize these parameters to develop an automated quality control procedure to identify and flag migrating-bird-contaminated Doppler velocity scans (sweeps). The first step is accomplished in this study (Part I) by identifying possible migrating-bird echoes in the level II data collected from the Oklahoma KTLX radar during the 2003 spring migrating season. The identifications are further verified by polarimetric radar measurements from the National Severe Storms Laboratory (NSSL) KOUN radar, Geostationary Operational Environmental Satellite (GOES) IR images, and rawinsonde measurements. Three proper parameters are found, and their histograms are prepared for the second step of development (reported in Part II).
Abstract
Based on the Bayesian statistical decision theory, a probabilistic quality control (QC) technique is developed to identify and flag migrating-bird-contaminated sweeps of level II velocity scans at the lowest elevation angle using the QC parameters presented in Part I. The QC technique can use either each single QC parameter or all three in combination. The single-parameter QC technique is shown to be useful for evaluating the effectiveness of each QC parameter based on the smallness of the tested percentages of wrong decision by using the ground truth information (if available) or based on the smallness of the estimated probabilities of wrong decision (if there is no ground truth information). The multiparameter QC technique is demonstrated to be much better than any of the three single-parameter QC techniques, as indicated by the very small value of the tested percentages of wrong decision for no-flag decisions (not contaminated by migrating birds). Since the averages of the estimated probabilities of wrong decision are quite close to the tested percentages of wrong decision, they can provide useful information about the probability of wrong decision when the multiparameter QC technique is used for real applications (with no ground truth information).
Abstract
Based on the Bayesian statistical decision theory, a probabilistic quality control (QC) technique is developed to identify and flag migrating-bird-contaminated sweeps of level II velocity scans at the lowest elevation angle using the QC parameters presented in Part I. The QC technique can use either each single QC parameter or all three in combination. The single-parameter QC technique is shown to be useful for evaluating the effectiveness of each QC parameter based on the smallness of the tested percentages of wrong decision by using the ground truth information (if available) or based on the smallness of the estimated probabilities of wrong decision (if there is no ground truth information). The multiparameter QC technique is demonstrated to be much better than any of the three single-parameter QC techniques, as indicated by the very small value of the tested percentages of wrong decision for no-flag decisions (not contaminated by migrating birds). Since the averages of the estimated probabilities of wrong decision are quite close to the tested percentages of wrong decision, they can provide useful information about the probability of wrong decision when the multiparameter QC technique is used for real applications (with no ground truth information).
Abstract
Retrospective forecasts of the new NCEP Climate Forecast System (CFS) have been analyzed out to 45 days from 1999 to 2009 with four members (0000, 0600, 1200, and 1800 UTC) each day. The new version of CFS [CFS, version 2 (CFSv2)] shows significant improvement over the older CFS [CFS, version 1 (CFSv1)] in predicting the Madden–Julian oscillation (MJO), with skill reaching 2–3 weeks in comparison with the CFSv1’s skill of nearly 1 week. Diagnostics of experiments related to the MJO forecast show that the systematic error correction, possible only because of the enormous hindcast dataset and the ensemble aspects of the prediction system (4 times a day), do contribute to improved forecasts. But the main reason is the improvement in the model and initial conditions between 1995 and 2010.
Abstract
Retrospective forecasts of the new NCEP Climate Forecast System (CFS) have been analyzed out to 45 days from 1999 to 2009 with four members (0000, 0600, 1200, and 1800 UTC) each day. The new version of CFS [CFS, version 2 (CFSv2)] shows significant improvement over the older CFS [CFS, version 1 (CFSv1)] in predicting the Madden–Julian oscillation (MJO), with skill reaching 2–3 weeks in comparison with the CFSv1’s skill of nearly 1 week. Diagnostics of experiments related to the MJO forecast show that the systematic error correction, possible only because of the enormous hindcast dataset and the ensemble aspects of the prediction system (4 times a day), do contribute to improved forecasts. But the main reason is the improvement in the model and initial conditions between 1995 and 2010.
Abstract
North Africa was green during the mid-Holocene [about 6000 years ago (6 ka)] and emitted much less dust to the atmosphere than in the present day. Here we use a fully coupled atmosphere–ocean general circulation model, CESM1.2.2, to test the impact of dust reduction and greening of the Sahara on the Atlantic meridional overturning circulation (AMOC) during this period. Results show that dust removal leads to a decrease of AMOC by 6.2% while greening of the Sahara with 100% shrub (100% grass) cover causes an enhancement of the AMOC by 6.1% (4.8%). The AMOC is increased by 5.3% (2.3%) when both the dust reduction and green Sahara with 100% shrub (100% grass) are considered. The AMOC changes are primarily due to the precipitation change over the west subtropical North Atlantic, from where the salinity anomaly is advected to the deep-water formation region. Global-mean surface temperature increases by 0.09° and 0.40°C (0.25°C) when global dust is removed and when North Africa and the Arabian region are covered by shrub (grass), respectively, showing a dominating effect of vegetation over dust. The comparison between modeled and reconstructed sea surface temperature is improved when the effect of vegetation is considered. The results may have implications for climate impact of future wetting over North Africa, either through global warming or through building of solar farms and wind farms.
Abstract
North Africa was green during the mid-Holocene [about 6000 years ago (6 ka)] and emitted much less dust to the atmosphere than in the present day. Here we use a fully coupled atmosphere–ocean general circulation model, CESM1.2.2, to test the impact of dust reduction and greening of the Sahara on the Atlantic meridional overturning circulation (AMOC) during this period. Results show that dust removal leads to a decrease of AMOC by 6.2% while greening of the Sahara with 100% shrub (100% grass) cover causes an enhancement of the AMOC by 6.1% (4.8%). The AMOC is increased by 5.3% (2.3%) when both the dust reduction and green Sahara with 100% shrub (100% grass) are considered. The AMOC changes are primarily due to the precipitation change over the west subtropical North Atlantic, from where the salinity anomaly is advected to the deep-water formation region. Global-mean surface temperature increases by 0.09° and 0.40°C (0.25°C) when global dust is removed and when North Africa and the Arabian region are covered by shrub (grass), respectively, showing a dominating effect of vegetation over dust. The comparison between modeled and reconstructed sea surface temperature is improved when the effect of vegetation is considered. The results may have implications for climate impact of future wetting over North Africa, either through global warming or through building of solar farms and wind farms.
Abstract
Hurricane Patricia (2015) broke records in both peak intensity and rapid intensification (RI) rate over the eastern Pacific basin. All of the then-operational models predicted less than half of its extraordinary intensity and RI rate, leaving a challenge for numerical modeling studies. In this study, a successful 42-h simulation of Patricia is obtained using a quintuply nested-grid version of the Weather Research and Forecast (WRF) Model with the finest grid size of 333 m. Results show that the WRF Model, initialized with the Global Forecast System Final Analysis data only, could reproduce the track, peak intensity, and many inner-core features, as verified against various observations. In particular, its simulated maximum surface wind of 92 m s−1 is close to the observed 95 m s−1, capturing the unprecedented RI rate of 54 m s−1 (24 h)−1. In addition, the model reproduces an intense warm-cored eye, a small-sized eyewall with a radius of maximum wind of less than 10 km, and the distribution of narrow spiral rainbands. A series of sensitivity simulations is performed to help understand which model configurations are essential to reproducing the extraordinary intensity of the storm. Results reveal that Patricia’s extraordinary development and its many inner-core structures could be reasonably well simulated if ultrahigh horizontal resolution, appropriate model physics, and realistic initial vortex intensity are incorporated. It is concluded that the large-scale conditions (e.g., warm sea surface temperature, weak vertical wind shear, and the moist intertropical convergence zone) and convective organization play important roles in determining the predictability of Patricia’s extraordinary RI and peak intensity.
Abstract
Hurricane Patricia (2015) broke records in both peak intensity and rapid intensification (RI) rate over the eastern Pacific basin. All of the then-operational models predicted less than half of its extraordinary intensity and RI rate, leaving a challenge for numerical modeling studies. In this study, a successful 42-h simulation of Patricia is obtained using a quintuply nested-grid version of the Weather Research and Forecast (WRF) Model with the finest grid size of 333 m. Results show that the WRF Model, initialized with the Global Forecast System Final Analysis data only, could reproduce the track, peak intensity, and many inner-core features, as verified against various observations. In particular, its simulated maximum surface wind of 92 m s−1 is close to the observed 95 m s−1, capturing the unprecedented RI rate of 54 m s−1 (24 h)−1. In addition, the model reproduces an intense warm-cored eye, a small-sized eyewall with a radius of maximum wind of less than 10 km, and the distribution of narrow spiral rainbands. A series of sensitivity simulations is performed to help understand which model configurations are essential to reproducing the extraordinary intensity of the storm. Results reveal that Patricia’s extraordinary development and its many inner-core structures could be reasonably well simulated if ultrahigh horizontal resolution, appropriate model physics, and realistic initial vortex intensity are incorporated. It is concluded that the large-scale conditions (e.g., warm sea surface temperature, weak vertical wind shear, and the moist intertropical convergence zone) and convective organization play important roles in determining the predictability of Patricia’s extraordinary RI and peak intensity.
Abstract
Predictability limits for seasonal atmospheric climate variability depend on the fraction of variability that is due to factors external to the atmosphere (e.g., boundary conditions) and the fraction that is internal. From the analysis of observed data alone, however, separation of the total seasonal atmospheric variance into its external and internal components remains a difficult and controversial issue. In this paper a simple procedure for estimating atmospheric internal variability is outlined. This procedure is based on the expected value of the mean square error between the observed and the general circulation model simulated (or predicted) seasonal mean anomaly. The end result is a spatial map for the estimate of the observed seasonal atmospheric internal (or unpredictable) variability. As improved general circulation models become available, mean square error estimated from the new generation of general circulation models can be easily included in the procedure proposed herein, bringing the estimate for the internal variability closer to its true estimate.
Abstract
Predictability limits for seasonal atmospheric climate variability depend on the fraction of variability that is due to factors external to the atmosphere (e.g., boundary conditions) and the fraction that is internal. From the analysis of observed data alone, however, separation of the total seasonal atmospheric variance into its external and internal components remains a difficult and controversial issue. In this paper a simple procedure for estimating atmospheric internal variability is outlined. This procedure is based on the expected value of the mean square error between the observed and the general circulation model simulated (or predicted) seasonal mean anomaly. The end result is a spatial map for the estimate of the observed seasonal atmospheric internal (or unpredictable) variability. As improved general circulation models become available, mean square error estimated from the new generation of general circulation models can be easily included in the procedure proposed herein, bringing the estimate for the internal variability closer to its true estimate.
Abstract
A temporal interpolation is required for three-dimensional Doppler wind analysis when the precise measurement time is counted for each radar beam position. The time interpolation is traditionally done by a linear scheme either in the measurement space or in the analysis space. Because a volume scan often takes 5–10 min, the linear time interpolation is not accurate enough to capture the rapidly changing winds associated with a fast-moving and fast-growing storm. Performing the linear interpolation in a frame moving with the storm can reduce the error, but the analyzed wind field is traditionally assumed to be stationary in the moving frame. The stationary assumption simplifies the computation but ignores the time variation of the true wind field in the moving frame. By incorporating a linear time interpolation into the moving frame wind analysis, an improved scheme is developed. The merits of the new scheme are demonstrated by idealized examples and numerical experiments with simulated radar observations. The new scheme is also applied to real radar data for a supercell storm.
Abstract
A temporal interpolation is required for three-dimensional Doppler wind analysis when the precise measurement time is counted for each radar beam position. The time interpolation is traditionally done by a linear scheme either in the measurement space or in the analysis space. Because a volume scan often takes 5–10 min, the linear time interpolation is not accurate enough to capture the rapidly changing winds associated with a fast-moving and fast-growing storm. Performing the linear interpolation in a frame moving with the storm can reduce the error, but the analyzed wind field is traditionally assumed to be stationary in the moving frame. The stationary assumption simplifies the computation but ignores the time variation of the true wind field in the moving frame. By incorporating a linear time interpolation into the moving frame wind analysis, an improved scheme is developed. The merits of the new scheme are demonstrated by idealized examples and numerical experiments with simulated radar observations. The new scheme is also applied to real radar data for a supercell storm.
Abstract
Unlike the classical point vortex model, a new method is developed to extract flows induced not only by vorticity but also by divergence in a well-defined vortex core area of a cyclone. This new method is applied to diagnosing the interactions of three midlatitude cyclones (called A, B, and C) that account for a missed summer severe rainfall forecast, in which the daily precipitation predicted by the Canadian operational model is an order of magnitude smaller than the rain gauge and radar measurements. In this event, cyclone B, responsible for the severe rainfall occurrence, was advected largely by flows induced by two neighboring cyclones: A and C to the west and east, respectively. This work attempts to assess whether and to what degree the vertical tilt of the observed cyclone versus that of the forecast cyclone B is caused by advections of the environmental flows (including A- and C-induced flows) at 500 and 1000 hPa. Results show that the observed cyclone B was advected mainly by the cyclone A–induced flow at 500 hPa into a vertically tilted structure that was northwestward against the vertical shear of the environmental flow and thus favorable for upward motion and cyclone intensification around the time of severe rainfall. However, the forecast cyclone B was advected largely by the cyclone A–induced flow at 500 hPa and the cyclone C–induced flow at 1000 hPa into an increasingly northward-tilted structure that was along the vertical shear of the environmental flow and thus unfavorable for upward motion and cyclone intensification, leading to the missed forecast of severe rainfall. Suggestions are made for future improvements of model forecasts.
Abstract
Unlike the classical point vortex model, a new method is developed to extract flows induced not only by vorticity but also by divergence in a well-defined vortex core area of a cyclone. This new method is applied to diagnosing the interactions of three midlatitude cyclones (called A, B, and C) that account for a missed summer severe rainfall forecast, in which the daily precipitation predicted by the Canadian operational model is an order of magnitude smaller than the rain gauge and radar measurements. In this event, cyclone B, responsible for the severe rainfall occurrence, was advected largely by flows induced by two neighboring cyclones: A and C to the west and east, respectively. This work attempts to assess whether and to what degree the vertical tilt of the observed cyclone versus that of the forecast cyclone B is caused by advections of the environmental flows (including A- and C-induced flows) at 500 and 1000 hPa. Results show that the observed cyclone B was advected mainly by the cyclone A–induced flow at 500 hPa into a vertically tilted structure that was northwestward against the vertical shear of the environmental flow and thus favorable for upward motion and cyclone intensification around the time of severe rainfall. However, the forecast cyclone B was advected largely by the cyclone A–induced flow at 500 hPa and the cyclone C–induced flow at 1000 hPa into an increasingly northward-tilted structure that was along the vertical shear of the environmental flow and thus unfavorable for upward motion and cyclone intensification, leading to the missed forecast of severe rainfall. Suggestions are made for future improvements of model forecasts.
