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- Author or Editor: Qing Wang x
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Abstract
Harmonic analysis of 10 yr of Ocean Topography Experiment (TOPEX)/Poseidon (T/P) along-track altimetry is performed to derive the semidiurnal and diurnal tides (M 2, S 2, N 2, K 2, K 1, O 1, P 1, and Q 1) near Hawaii. The T/P solutions are evaluated through intercomparison for crossover points of the ascending and descending tracks and comparison with the data of tidal stations, which show that the T/P solutions in the study area are reliable. By using a suitable order polynomial to fit the T/P solutions along every track, the harmonic constants of any point on T/P tracks are acquired. A new fitting method, which is characterized by applying the harmonics from T/P tracks to produce directly empirical cotidal charts, is developed. The harmonic constants derived by this fitting method show good agreement with the data of tidal stations, the results of National Astronomical Observatory 99b (NAO.99b), TOPEX/Poseidon 7.2 (TPXO7.2), and Finite Element Solutions 2004 (FES2004) models, which suggests that the fitting method is reasonable, and the highly accurate cotidal chart could be directly acquired from T/P altimetry data by this fitting method.
Abstract
Harmonic analysis of 10 yr of Ocean Topography Experiment (TOPEX)/Poseidon (T/P) along-track altimetry is performed to derive the semidiurnal and diurnal tides (M 2, S 2, N 2, K 2, K 1, O 1, P 1, and Q 1) near Hawaii. The T/P solutions are evaluated through intercomparison for crossover points of the ascending and descending tracks and comparison with the data of tidal stations, which show that the T/P solutions in the study area are reliable. By using a suitable order polynomial to fit the T/P solutions along every track, the harmonic constants of any point on T/P tracks are acquired. A new fitting method, which is characterized by applying the harmonics from T/P tracks to produce directly empirical cotidal charts, is developed. The harmonic constants derived by this fitting method show good agreement with the data of tidal stations, the results of National Astronomical Observatory 99b (NAO.99b), TOPEX/Poseidon 7.2 (TPXO7.2), and Finite Element Solutions 2004 (FES2004) models, which suggests that the fitting method is reasonable, and the highly accurate cotidal chart could be directly acquired from T/P altimetry data by this fitting method.
Abstract
To investigate the optimum length of time series (TS) for harmonic analysis (HA) in the simulation of multiple constituents, a two-dimensional tidal model is used to simulate the M2, S2, K1, and O1 constituents in the Bohai and Yellow Seas. By analyzing the HA results of several nonoverlapping TS of the same length, which varies from 15 to 365 days, a field-average deviation of HA results is calculated. A deviation that is sufficiently small means that HA results are independent of the choice of TS, and the corresponding TS length is regarded as the optimum. Results indicate that the range of 180–195 days is the optimum length of TS for HA in the simulation of the four principal constituents. To investigate what determines the optimum length, experiments with different computed area and model settings are carried out. Results indicate that the optimum length is independent of advection, nodal corrections, and computed area, and only depends on bottom friction. Nonlinear bottom friction results in the appearance of higher harmonics and explains why the optimum length of TS for HA is 180–195 days.
Abstract
To investigate the optimum length of time series (TS) for harmonic analysis (HA) in the simulation of multiple constituents, a two-dimensional tidal model is used to simulate the M2, S2, K1, and O1 constituents in the Bohai and Yellow Seas. By analyzing the HA results of several nonoverlapping TS of the same length, which varies from 15 to 365 days, a field-average deviation of HA results is calculated. A deviation that is sufficiently small means that HA results are independent of the choice of TS, and the corresponding TS length is regarded as the optimum. Results indicate that the range of 180–195 days is the optimum length of TS for HA in the simulation of the four principal constituents. To investigate what determines the optimum length, experiments with different computed area and model settings are carried out. Results indicate that the optimum length is independent of advection, nodal corrections, and computed area, and only depends on bottom friction. Nonlinear bottom friction results in the appearance of higher harmonics and explains why the optimum length of TS for HA is 180–195 days.
Abstract
Complex terrain poses challenges to the ground-based radar quantitative precipitation estimation (QPE) because of partial or total blockages of radar beams in the lower tilts. Reflectivities from higher tilts are often used in the QPE under these circumstances and biases are then introduced due to vertical variations of reflectivity. The spaceborne Precipitation Radar (PR) on board the Tropical Rainfall Measuring Mission (TRMM) satellite can provide good measurements of the vertical structure of reflectivity even in complex terrain, but the poor temporal resolution of TRMM PR data limits their usefulness in real-time QPE. This study proposes a novel vertical profile of reflectivity (VPR) correction approach to enhance ground radar–based QPEs in complex terrain by integrating the spaceborne radar observations. In the current study, climatological relationships between VPRs from an S-band Doppler weather radar located on the east coast of Taiwan and the TRMM PR are developed using an artificial neural network (ANN). When a lower tilt of the ground radar is blocked, higher-tilt reflectivity data are corrected with the trained ANN and then applied in the rainfall estimation. The proposed algorithm was evaluated with three typhoon precipitation events, and its preliminary performance was evaluated and analyzed.
Abstract
Complex terrain poses challenges to the ground-based radar quantitative precipitation estimation (QPE) because of partial or total blockages of radar beams in the lower tilts. Reflectivities from higher tilts are often used in the QPE under these circumstances and biases are then introduced due to vertical variations of reflectivity. The spaceborne Precipitation Radar (PR) on board the Tropical Rainfall Measuring Mission (TRMM) satellite can provide good measurements of the vertical structure of reflectivity even in complex terrain, but the poor temporal resolution of TRMM PR data limits their usefulness in real-time QPE. This study proposes a novel vertical profile of reflectivity (VPR) correction approach to enhance ground radar–based QPEs in complex terrain by integrating the spaceborne radar observations. In the current study, climatological relationships between VPRs from an S-band Doppler weather radar located on the east coast of Taiwan and the TRMM PR are developed using an artificial neural network (ANN). When a lower tilt of the ground radar is blocked, higher-tilt reflectivity data are corrected with the trained ANN and then applied in the rainfall estimation. The proposed algorithm was evaluated with three typhoon precipitation events, and its preliminary performance was evaluated and analyzed.
Abstract
A numerical study using the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) was performed to assess the impact of initial and boundary conditions, the parameterization of turbulence transfer and its coupling with cloud-driven radiation, and cloud microphysical processes on the accuracy of mesoscale predictions and forecasts of the cloud-capped marine boundary layer. Aircraft, buoy, and satellite data and the large eddy simulation (LES) results during the Dynamics and Chemistry of Marine Stratocumulus field experiment (DYCOMS II) in July 2001 were used in the assessment. Three of the tested input fields (Eta, NCEP, and ECMWF) show deficiencies, mainly in the thermodynamic structure of the lowest 1500 m of the marine atmosphere. On a positive note, the simulated marine-layer depth showed good agreement with aircraft observations using the Eta fields, while using the NCEP and ECMWF datasets underestimated the marine-layer depth by about 20%–30%. The predicted turbulence kinetic energy (inversion strength) was about 50% of that obtained from the LES results (aircraft observed). As a consequence of moisture overprediction, the predicted liquid water path was twice the observed by 1–2 g kg−1. The sensitivity tests have shown that the selections of turbulence and cloud microphysical schemes significantly influence the turbulence estimates and cloud parameters. Two of the tested turbulence schemes (Eta PBL and Burk–Thompson) did not exhibit the coupling with radiation. The significant differences in the simulated turbulence estimates appear to be a consequence of the use of water-conserving potential temperature variables. The microphysical parameterization, which uses the number concentration of cloud drops in the autoconversion process, simulates a realistic evolution of precipitable hydrometeors in the cloudy marine layer on the positive side, but on the other hand enhances the decoupling in the turbulence structure. This study can provide guidance to operational forecasters concerning accuracy issues of the commonly used large-scale analyses for model initialization, and optimal selection of model parameterizations in order to simulate and forecast the cloudy atmospheric boundary layer over the ocean.
Abstract
A numerical study using the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) was performed to assess the impact of initial and boundary conditions, the parameterization of turbulence transfer and its coupling with cloud-driven radiation, and cloud microphysical processes on the accuracy of mesoscale predictions and forecasts of the cloud-capped marine boundary layer. Aircraft, buoy, and satellite data and the large eddy simulation (LES) results during the Dynamics and Chemistry of Marine Stratocumulus field experiment (DYCOMS II) in July 2001 were used in the assessment. Three of the tested input fields (Eta, NCEP, and ECMWF) show deficiencies, mainly in the thermodynamic structure of the lowest 1500 m of the marine atmosphere. On a positive note, the simulated marine-layer depth showed good agreement with aircraft observations using the Eta fields, while using the NCEP and ECMWF datasets underestimated the marine-layer depth by about 20%–30%. The predicted turbulence kinetic energy (inversion strength) was about 50% of that obtained from the LES results (aircraft observed). As a consequence of moisture overprediction, the predicted liquid water path was twice the observed by 1–2 g kg−1. The sensitivity tests have shown that the selections of turbulence and cloud microphysical schemes significantly influence the turbulence estimates and cloud parameters. Two of the tested turbulence schemes (Eta PBL and Burk–Thompson) did not exhibit the coupling with radiation. The significant differences in the simulated turbulence estimates appear to be a consequence of the use of water-conserving potential temperature variables. The microphysical parameterization, which uses the number concentration of cloud drops in the autoconversion process, simulates a realistic evolution of precipitable hydrometeors in the cloudy marine layer on the positive side, but on the other hand enhances the decoupling in the turbulence structure. This study can provide guidance to operational forecasters concerning accuracy issues of the commonly used large-scale analyses for model initialization, and optimal selection of model parameterizations in order to simulate and forecast the cloudy atmospheric boundary layer over the ocean.
Abstract
This study was designed to provide basic information for the improvement of storm nowcasting. According to the mean direction deviation of storm movement, storms were classified into three types: 1) steady storms (S storms, extrapolated efficiently), 2) unsteady storms (U storms, extrapolated poorly), and 3) transitional storms (T storms). The U storms do not fit the linear extrapolation processes because of their unsteady movements. A 6-yr warm-season radar observation dataset was used to highlight and analyze the differences between U storms and S storms. The analysis included geometric features, dynamic factors, and environmental parameters. The results showed that storms with the following characteristics changed movement direction most easily in the Beijing–Tianjin region: 1) smaller storm area, 2) lower thickness (echo-top height minus base height), 3) lower movement speed, 4) weaker updrafts and the maximum value located in the mid- and upper troposphere, 5) storm-relative vertical wind profiles dominated by directional shear instead of speed shear, 6) lower relative humidity in the mid- and upper troposphere, and 7) higher surface evaporation and ground roughness.
Abstract
This study was designed to provide basic information for the improvement of storm nowcasting. According to the mean direction deviation of storm movement, storms were classified into three types: 1) steady storms (S storms, extrapolated efficiently), 2) unsteady storms (U storms, extrapolated poorly), and 3) transitional storms (T storms). The U storms do not fit the linear extrapolation processes because of their unsteady movements. A 6-yr warm-season radar observation dataset was used to highlight and analyze the differences between U storms and S storms. The analysis included geometric features, dynamic factors, and environmental parameters. The results showed that storms with the following characteristics changed movement direction most easily in the Beijing–Tianjin region: 1) smaller storm area, 2) lower thickness (echo-top height minus base height), 3) lower movement speed, 4) weaker updrafts and the maximum value located in the mid- and upper troposphere, 5) storm-relative vertical wind profiles dominated by directional shear instead of speed shear, 6) lower relative humidity in the mid- and upper troposphere, and 7) higher surface evaporation and ground roughness.
Abstract
This paper intends to investigate the time scales of land surface hydrology and enhance the understanding of the hydrological cycle between the atmosphere, vegetation, and soil. A three-layer model for land surface hydrology is developed to study the temporal variation and vertical structure of water reservoirs in the vegetation–soil system in response to precipitation forcing. The model is an extension of the existing one-layer bucket model. A new time scale is derived, and it better represents the response time scale of soil moisture in the root zone than the previously derived inherent time scale (i.e., the ratio of the field capacity to the potential evaporation). It is found that different water reservoirs of the vegetation–soil system have different time scales. Precipitation forcing is mainly concentrated on short time scales with small low-frequency components, but it can cause long time-scale disturbances in the soil moisture of root zone. This time scale increases with soil depth, but it can be reduced significantly under wetter conditions. Although the time scale of total water content in the vertical column in the three-layer model is similar to that of the one-layer bucket model, the time scale of evapotranspiration is very different. This suggests the need to consider the vertical structure in land surface hydrology reservoirs and in climate study.
Abstract
This paper intends to investigate the time scales of land surface hydrology and enhance the understanding of the hydrological cycle between the atmosphere, vegetation, and soil. A three-layer model for land surface hydrology is developed to study the temporal variation and vertical structure of water reservoirs in the vegetation–soil system in response to precipitation forcing. The model is an extension of the existing one-layer bucket model. A new time scale is derived, and it better represents the response time scale of soil moisture in the root zone than the previously derived inherent time scale (i.e., the ratio of the field capacity to the potential evaporation). It is found that different water reservoirs of the vegetation–soil system have different time scales. Precipitation forcing is mainly concentrated on short time scales with small low-frequency components, but it can cause long time-scale disturbances in the soil moisture of root zone. This time scale increases with soil depth, but it can be reduced significantly under wetter conditions. Although the time scale of total water content in the vertical column in the three-layer model is similar to that of the one-layer bucket model, the time scale of evapotranspiration is very different. This suggests the need to consider the vertical structure in land surface hydrology reservoirs and in climate study.
Abstract
A new bulk transfer formulation for the surface turbulent fluxes of momentum, heat, and moisture has been developed by using the square root of the vertically averaged turbulent kinetic energy (TKE) in the atmospheric boundary layer as a velocity scale, in place of the mean wind speed. The new parameterization utilizes the surface radiative (skin) temperature instead of the temperature at a “roughness height.” Field observations and large-eddy simulation (LES) results were used to develop the parameterization. It has been tested using an independent dataset from the First ISLSCP (International Satellite Land Surface Climatology Project) Field Experiment (FIFE). The predicted surface momentum flux compares very well with the observations, despite the fact that the data used for developing the new parameterization have a very different roughness length from the independent FIFE data. This shows that the parameterization can represent a wide range of surface roughness boundary conditions. The predicted sensible and latent heat fluxes also agree well with the FIFE observations, although the predicted surface sensible heat flux is somewhat less than observed at the FIFE site. The diurnal cycles of the predicted surface sensible heat and latent heat fluxes correspond very well with the observations in both magnitude and phase.
Abstract
A new bulk transfer formulation for the surface turbulent fluxes of momentum, heat, and moisture has been developed by using the square root of the vertically averaged turbulent kinetic energy (TKE) in the atmospheric boundary layer as a velocity scale, in place of the mean wind speed. The new parameterization utilizes the surface radiative (skin) temperature instead of the temperature at a “roughness height.” Field observations and large-eddy simulation (LES) results were used to develop the parameterization. It has been tested using an independent dataset from the First ISLSCP (International Satellite Land Surface Climatology Project) Field Experiment (FIFE). The predicted surface momentum flux compares very well with the observations, despite the fact that the data used for developing the new parameterization have a very different roughness length from the independent FIFE data. This shows that the parameterization can represent a wide range of surface roughness boundary conditions. The predicted sensible and latent heat fluxes also agree well with the FIFE observations, although the predicted surface sensible heat flux is somewhat less than observed at the FIFE site. The diurnal cycles of the predicted surface sensible heat and latent heat fluxes correspond very well with the observations in both magnitude and phase.
Abstract
A probabilistic spatiotemporal approach based on a spatial regression test (SRT-PS) is proposed for the quality control of climate data. It provides a quantitative probability that represents the uncertainty in each temperature observation. The assumption of SRT-PS is that there might be large uncertainty in the station record if there is a large residual difference between the record estimated in the spatial regression test and the true station record. The result of SRT-PS is expressed as a confidence probability ranging from 0 to 1, where a value closer to 1 indicates less uncertainty. The potential of SRT-PS to estimate quantitatively the uncertainty in temperature observations was demonstrated using an annual temperature dataset for China for the period 1971–2000 with seeded errors. SRT-PS was also applied to assess a real dataset, and was compared with two traditional quality control approaches: biweight mean and biweight standard deviation and SRT. The study provides a new approach to assess quantitatively the uncertainty in temperature observations at meteorological stations.
Abstract
A probabilistic spatiotemporal approach based on a spatial regression test (SRT-PS) is proposed for the quality control of climate data. It provides a quantitative probability that represents the uncertainty in each temperature observation. The assumption of SRT-PS is that there might be large uncertainty in the station record if there is a large residual difference between the record estimated in the spatial regression test and the true station record. The result of SRT-PS is expressed as a confidence probability ranging from 0 to 1, where a value closer to 1 indicates less uncertainty. The potential of SRT-PS to estimate quantitatively the uncertainty in temperature observations was demonstrated using an annual temperature dataset for China for the period 1971–2000 with seeded errors. SRT-PS was also applied to assess a real dataset, and was compared with two traditional quality control approaches: biweight mean and biweight standard deviation and SRT. The study provides a new approach to assess quantitatively the uncertainty in temperature observations at meteorological stations.
Abstract
Some climate datasets are incomplete at certain places and times. A novel technique called the point estimation model of Biased Sentinel Hospitals-based Area Disease Estimation (P-BSHADE) is introduced to interpolate missing data in temperature datasets. Effectiveness of the technique was empirically evaluated in terms of an annual temperature dataset from 1950 to 2000 in China. The P-BSHADE technique uses a weighted summation of observed stations to derive unbiased and minimum error variance estimates of missing data. Both the ratio and covariance between stations were used in calculation of these weights. In this way, interpolation of missing data in the temperature dataset was improved, and best linear unbiased estimates (BLUE) were obtained. Using the same dataset, performance of P-BSHADE was compared against three estimators: kriging, inverse distance weighting (IDW), and spatial regression test (SRT). Kriging and IDW assume a homogeneous stochastic field, which may not be the case. SRT employs spatiotemporal data and has the potential to consider temperature nonhomogeneity caused by topographic differences, but has no objective function for the BLUE. Instead, P-BSHADE takes into account geographic spatial autocorrelation and nonhomogeneity, and maximizes an objective function for the BLUE of the target station. In addition to the theoretical advantages of P-BSHADE over the three other methods, case studies for an annual Chinese temperature dataset demonstrate its empirical superiority, except for the SRT from 1950 to 1970.
Abstract
Some climate datasets are incomplete at certain places and times. A novel technique called the point estimation model of Biased Sentinel Hospitals-based Area Disease Estimation (P-BSHADE) is introduced to interpolate missing data in temperature datasets. Effectiveness of the technique was empirically evaluated in terms of an annual temperature dataset from 1950 to 2000 in China. The P-BSHADE technique uses a weighted summation of observed stations to derive unbiased and minimum error variance estimates of missing data. Both the ratio and covariance between stations were used in calculation of these weights. In this way, interpolation of missing data in the temperature dataset was improved, and best linear unbiased estimates (BLUE) were obtained. Using the same dataset, performance of P-BSHADE was compared against three estimators: kriging, inverse distance weighting (IDW), and spatial regression test (SRT). Kriging and IDW assume a homogeneous stochastic field, which may not be the case. SRT employs spatiotemporal data and has the potential to consider temperature nonhomogeneity caused by topographic differences, but has no objective function for the BLUE. Instead, P-BSHADE takes into account geographic spatial autocorrelation and nonhomogeneity, and maximizes an objective function for the BLUE of the target station. In addition to the theoretical advantages of P-BSHADE over the three other methods, case studies for an annual Chinese temperature dataset demonstrate its empirical superiority, except for the SRT from 1950 to 1970.
Abstract
Spring persistent rainfall (SPR) over southern China has great impact on its society and economics. A remarkable feature of the SPR is high frequency. However, SPR frequency obviously decreases over the period of 1997–2011. In this study, the possible causes have been investigated from the perspective of the individual and concurrent effects of the East Asian subtropical jet (EASJ) and East Asian polar front jet (EAPJ). A close relationship is detected between SPR frequency and EASJ intensity (but not EAPJ intensity). Associated with strong EASJ, abundant water vapor is transported to southern China by the southwesterly flow, which may trigger the SPR. Additionally, frequencies of both strong EASJ and weak EAPJ events are positively correlated with SPR frequency. Further investigation of the concurrent effect indicates a significant positive correlation between the frequencies of SPR and the strong EASJ–weak EAPJ configuration. Associated with this configuration, southwesterly flow strengthens in the lower troposphere, while northerly wind weakens in the upper troposphere. This provides a dynamic and moist condition, as enhanced ascending motion and intensified convergence of abundant water vapor over southern China, which favors the SPR. All analyses suggest that the EASJ may play a dominant role in the SPR occurrence and that the EAPJ may play a modulation role. Finally, a possible mechanism maintaining the strong EASJ–weak EAPJ configuration is proposed. Significant cooling over the northeastern Tibetan Plateau may induce a cyclone anomaly in the upper troposphere, which could result in an accelerating EASJ and a decelerating EAPJ.
Abstract
Spring persistent rainfall (SPR) over southern China has great impact on its society and economics. A remarkable feature of the SPR is high frequency. However, SPR frequency obviously decreases over the period of 1997–2011. In this study, the possible causes have been investigated from the perspective of the individual and concurrent effects of the East Asian subtropical jet (EASJ) and East Asian polar front jet (EAPJ). A close relationship is detected between SPR frequency and EASJ intensity (but not EAPJ intensity). Associated with strong EASJ, abundant water vapor is transported to southern China by the southwesterly flow, which may trigger the SPR. Additionally, frequencies of both strong EASJ and weak EAPJ events are positively correlated with SPR frequency. Further investigation of the concurrent effect indicates a significant positive correlation between the frequencies of SPR and the strong EASJ–weak EAPJ configuration. Associated with this configuration, southwesterly flow strengthens in the lower troposphere, while northerly wind weakens in the upper troposphere. This provides a dynamic and moist condition, as enhanced ascending motion and intensified convergence of abundant water vapor over southern China, which favors the SPR. All analyses suggest that the EASJ may play a dominant role in the SPR occurrence and that the EAPJ may play a modulation role. Finally, a possible mechanism maintaining the strong EASJ–weak EAPJ configuration is proposed. Significant cooling over the northeastern Tibetan Plateau may induce a cyclone anomaly in the upper troposphere, which could result in an accelerating EASJ and a decelerating EAPJ.
