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- Author or Editor: Ping Huang x
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Abstract
This note evaluates the numerical schemes used for computing the axial component of the mountain torque from gridded global surface pressure and topography datasets. It is shown that the two formulas of the mountain torque based on (i) an integral of the product of the surface pressure and the gradient of topography, and (ii) an integral of the product of the topography and the surface pressure gradient, should produce identical results if a centered even-ordered finite-difference scheme or the spectral method is used to evaluate the integrand. Noncentered finite-difference schemes are not recommended not only because they produce extremely large errors but also because they produce different results for the two formulas. When compared with the benchmark calculation using the spectral method, it is found that the centered fourth-order finite-difference scheme is an efficient and generally accurate approximation for practical applications. Using the data from NCEP–NCAR reanalysis, the finite-difference schemes generally underestimate the global mountain torque compared to the benchmark. This negative error is interpreted as due to the asymmetry in the distribution of surface pressure and in the steepness of the topography between the western and eastern slopes of the mountains.
Abstract
This note evaluates the numerical schemes used for computing the axial component of the mountain torque from gridded global surface pressure and topography datasets. It is shown that the two formulas of the mountain torque based on (i) an integral of the product of the surface pressure and the gradient of topography, and (ii) an integral of the product of the topography and the surface pressure gradient, should produce identical results if a centered even-ordered finite-difference scheme or the spectral method is used to evaluate the integrand. Noncentered finite-difference schemes are not recommended not only because they produce extremely large errors but also because they produce different results for the two formulas. When compared with the benchmark calculation using the spectral method, it is found that the centered fourth-order finite-difference scheme is an efficient and generally accurate approximation for practical applications. Using the data from NCEP–NCAR reanalysis, the finite-difference schemes generally underestimate the global mountain torque compared to the benchmark. This negative error is interpreted as due to the asymmetry in the distribution of surface pressure and in the steepness of the topography between the western and eastern slopes of the mountains.
Abstract
The use of the Advanced Very High Resolution Radiometer (AVHRR) data in a one-dimensional variational scheme is examined to retrieve cloud parameters and atmospheric profiles. The variational scheme used TIROS Operational Vertical Sounder radiance data for retrieval. The AVHRR data were used in the partly cloudy and cloudy cases to provide initial guesses for cloud parameters in the iterative scheme, to detect the presence of cirrus clouds, and to determine the sea surface temperature used in retrieval. Sensitivity tests showed that the error in the initial guesses of cloud parameters has substantial impact on the accuracy of the retrieved fields; this sensitivity increases with increased cloudiness. Cloud parameters deduced from AVHRR data are nearly optimal, in terms of maximizing the efficiency of convergence, as the initial guesses for the retrieval scheme. In the absence of cirrus cloud, a retrieval procedure incorporating AVHRR initial guesses produced temperature and humidity profiles for partly cloudy cases that are about as accurate as those for clear cases. In both cases the maximum improvement made in the retrieval procedure over background error was about 0.2 K in the temperature profile, and 0.05 (in logarithm of mixing ratio) in the humidity profile. For partly cloudy cases, best retrieval results were obtained for a low cloud top, or a middle cloud top but with small cloud fraction. Cirrus cloud remains a problem, as its presence generally degrades the quality of retrieval.
Abstract
The use of the Advanced Very High Resolution Radiometer (AVHRR) data in a one-dimensional variational scheme is examined to retrieve cloud parameters and atmospheric profiles. The variational scheme used TIROS Operational Vertical Sounder radiance data for retrieval. The AVHRR data were used in the partly cloudy and cloudy cases to provide initial guesses for cloud parameters in the iterative scheme, to detect the presence of cirrus clouds, and to determine the sea surface temperature used in retrieval. Sensitivity tests showed that the error in the initial guesses of cloud parameters has substantial impact on the accuracy of the retrieved fields; this sensitivity increases with increased cloudiness. Cloud parameters deduced from AVHRR data are nearly optimal, in terms of maximizing the efficiency of convergence, as the initial guesses for the retrieval scheme. In the absence of cirrus cloud, a retrieval procedure incorporating AVHRR initial guesses produced temperature and humidity profiles for partly cloudy cases that are about as accurate as those for clear cases. In both cases the maximum improvement made in the retrieval procedure over background error was about 0.2 K in the temperature profile, and 0.05 (in logarithm of mixing ratio) in the humidity profile. For partly cloudy cases, best retrieval results were obtained for a low cloud top, or a middle cloud top but with small cloud fraction. Cirrus cloud remains a problem, as its presence generally degrades the quality of retrieval.
Abstract
An intercomparison of the reanalysis datasets of NCEP and ECMWF is performed with respect to the response of the axial angular momentum M to the torques. While both sets satisfy the budget equations of M reasonably well (except for the time mean), this is not the case with respect to the budget equations for the difference of both sets, where the analysis data explain only a small fraction of the difference of the angular momenta in terms of the difference of the torques. It is hypothesized that the larger fraction of the difference is a manifestation of analysis error. The autocorrelation functions of the differences of the mountain and friction torques between both sets exhibit a long memory, which reflects errors in the low-frequency components of the datasets. Probability distributions of M are considered as well. It is shown that the mean torque for a given M has to be positive (negative) for negative (positive) deviations of M. It is found that the NCEP torques, as analyzed, satisfy this basic requirement. The distribution of the difference of the angular momenta cannot be explained on the basis of the corresponding difference of the torques.
Abstract
An intercomparison of the reanalysis datasets of NCEP and ECMWF is performed with respect to the response of the axial angular momentum M to the torques. While both sets satisfy the budget equations of M reasonably well (except for the time mean), this is not the case with respect to the budget equations for the difference of both sets, where the analysis data explain only a small fraction of the difference of the angular momenta in terms of the difference of the torques. It is hypothesized that the larger fraction of the difference is a manifestation of analysis error. The autocorrelation functions of the differences of the mountain and friction torques between both sets exhibit a long memory, which reflects errors in the low-frequency components of the datasets. Probability distributions of M are considered as well. It is shown that the mean torque for a given M has to be positive (negative) for negative (positive) deviations of M. It is found that the NCEP torques, as analyzed, satisfy this basic requirement. The distribution of the difference of the angular momenta cannot be explained on the basis of the corresponding difference of the torques.
Abstract
The FORMOSAT-7/COSMIC-2 Global Navigation Satellite System (GNSS) Radio Occultation (RO) satellite constellation was launched in June 2019 as a successor of the FORMOSAT-3/COSMIC mission. The Central Weather Bureau (CWB) of Taiwan has received FORMOSAT-7/COSMIC-2 GNSS RO data in real time from the Taiwan Analysis Center for COSMIC. With the global numerical prediction system at CWB, a parallel semioperational experiment assimilating the FORMOSAT-7/COSMIC-2 bending angle data with all other operational observation data has been conducted to evaluate the impact of the FORMOSAT-7/COSMIC-2 data. The first seven-month results show that the quality of the early FORMOSAT-7/COSMIC-2 data has been satisfactory for assimilation. Consistent and significant positive impacts on global forecast skills have been observed since the start of the parallel experiment, with the most significant impact found in the tropical region, reflecting the low-inclination orbital design of the satellites. The impact of the FORMOSAT-7/COSMIC-2 RO data is also estimated using the ensemble forecast sensitivity to observation impact (EFSOI) method, showing an average positive impact per observation similar to other existing GNSS RO datasets, while the total impact is impressive by virtue of its large amount. Sensitivity experiments suggest that the quality control processes built in the Gridpoint Statistical Interpolation (GSI) system for RO data work well to achieve a positive impact by the low-level FORMOSAT-7/COSMIC-2 RO data, while more effort on observation error tuning should be focused to obtain an optimal assimilation performance. This study demonstrates the usefulness of the FORMOSAT-7/COSMIC-2 RO data in global numerical weather prediction during the calibration/validation period and leads to the operational use of the data at CWB.
Abstract
The FORMOSAT-7/COSMIC-2 Global Navigation Satellite System (GNSS) Radio Occultation (RO) satellite constellation was launched in June 2019 as a successor of the FORMOSAT-3/COSMIC mission. The Central Weather Bureau (CWB) of Taiwan has received FORMOSAT-7/COSMIC-2 GNSS RO data in real time from the Taiwan Analysis Center for COSMIC. With the global numerical prediction system at CWB, a parallel semioperational experiment assimilating the FORMOSAT-7/COSMIC-2 bending angle data with all other operational observation data has been conducted to evaluate the impact of the FORMOSAT-7/COSMIC-2 data. The first seven-month results show that the quality of the early FORMOSAT-7/COSMIC-2 data has been satisfactory for assimilation. Consistent and significant positive impacts on global forecast skills have been observed since the start of the parallel experiment, with the most significant impact found in the tropical region, reflecting the low-inclination orbital design of the satellites. The impact of the FORMOSAT-7/COSMIC-2 RO data is also estimated using the ensemble forecast sensitivity to observation impact (EFSOI) method, showing an average positive impact per observation similar to other existing GNSS RO datasets, while the total impact is impressive by virtue of its large amount. Sensitivity experiments suggest that the quality control processes built in the Gridpoint Statistical Interpolation (GSI) system for RO data work well to achieve a positive impact by the low-level FORMOSAT-7/COSMIC-2 RO data, while more effort on observation error tuning should be focused to obtain an optimal assimilation performance. This study demonstrates the usefulness of the FORMOSAT-7/COSMIC-2 RO data in global numerical weather prediction during the calibration/validation period and leads to the operational use of the data at CWB.
Abstract
This study evaluates the impact of dust–radiation–cloud interactions on the development of a mesoscale convective system (MCS) by comparing numerical experiments run with and without dust–radiation and/or dust–cloud interactions. An MCS that developed over North Africa on 4–6 July 2010 is used as a case study. The CloudSat and Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellites passed over the center of the MCS after it reached maturity, providing valuable profiles of aerosol backscatter and cloud information for model verification. The model best reproduces the MCS’s observed cloud structure and morphology when both dust–radiation and dust–cloud interactions are included. Our results indicate that the dust–radiation effect has a far greater influence on the MCS’s development than the dust-cloud effect. Results show that the dust-radiative effect, both with and without the dust–cloud interaction, briefly delays the MCS’s formation but ultimately produces a stronger storm with a more extensive anvil cloud. This is caused by dust–radiation-induced changes to the MCS’s environment. The impact of the dust–cloud effect on the MCS, on the other hand, is greatly affected by the presence of the dust–radiation interaction. The dust–cloud effect alone slows initial cloud development but enhances heterogeneous ice nucleation and extends cloud lifetime. When the dust–radiation interaction is added, increased transport of dust into the upper portions of the storm—due to a dust–radiation-driven increase in convective intensity—allows dust–cloud processes to more significantly enhance heterogeneous freezing activity earlier in the storm’s development, increasing updraft strength, hydrometeor growth (particularly for ice particles), and rainfall.
Abstract
This study evaluates the impact of dust–radiation–cloud interactions on the development of a mesoscale convective system (MCS) by comparing numerical experiments run with and without dust–radiation and/or dust–cloud interactions. An MCS that developed over North Africa on 4–6 July 2010 is used as a case study. The CloudSat and Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellites passed over the center of the MCS after it reached maturity, providing valuable profiles of aerosol backscatter and cloud information for model verification. The model best reproduces the MCS’s observed cloud structure and morphology when both dust–radiation and dust–cloud interactions are included. Our results indicate that the dust–radiation effect has a far greater influence on the MCS’s development than the dust-cloud effect. Results show that the dust-radiative effect, both with and without the dust–cloud interaction, briefly delays the MCS’s formation but ultimately produces a stronger storm with a more extensive anvil cloud. This is caused by dust–radiation-induced changes to the MCS’s environment. The impact of the dust–cloud effect on the MCS, on the other hand, is greatly affected by the presence of the dust–radiation interaction. The dust–cloud effect alone slows initial cloud development but enhances heterogeneous ice nucleation and extends cloud lifetime. When the dust–radiation interaction is added, increased transport of dust into the upper portions of the storm—due to a dust–radiation-driven increase in convective intensity—allows dust–cloud processes to more significantly enhance heterogeneous freezing activity earlier in the storm’s development, increasing updraft strength, hydrometeor growth (particularly for ice particles), and rainfall.
