Search Results

You are looking at 1 - 8 of 8 items for

  • Author or Editor: Chi O. Ao x
  • Refine by Access: All Content x
Clear All Modify Search
Lan Luan, Paul W. Staten, Chi O. Ao, and Qiang Fu

Abstract

The width of the tropical belt has been analyzed with a variety of metrics, often based on zonal-mean data from reanalyses. However, constraining the global and regional tropical width requires both a global spatial-resolving observational dataset and an appropriate metric to take advantage of such data. The tropical tropopause break is arguably such a metric. This study aims to evaluate the performance of different reanalyses and metrics with a focus on depicting regional tropical belt width. We choose four distinct tropopause-break metrics derived from global positioning system radio occultation (GPS-RO) satellite data and four modern reanalyses (ERA-Interim, MERRA-2, JRA-55, and CFSR). We show that reanalyses generally reproduce the regional tropical tropopause break to within 10° of that in GPS-RO data—but that the tropical width is somewhat sensitive (within 4°) to how data are averaged zonally, moderately sensitive (within 10°) to the dataset resolution, and more sensitive (20° over the Northern Hemisphere Atlantic Ocean during June–August) to the choice of metric. Reanalyses capture the poleward displacement of the tropical tropopause break over land and equatorward displacement over ocean during summertime, and the reverse during the wintertime. Reanalysis-based tropopause breaks are also generally well correlated with those from GPS-RO, although CFSR reproduces 14-yr trends much more closely than others (including ERA-Interim). However, it is hard to say which dataset is the best match of GPS-RO. We further find that the tropical tropopause break is representative of the subtropical jet latitude and the Northern Hemisphere edge of the Hadley circulation in terms of year-to-year variations.

Free access
Olga P. Verkhoglyadova, Stephen S. Leroy, and Chi O. Ao

Abstract

GPS radio occultations (RO) offer the possibility to map winds in the upper troposphere and lower stratosphere (UTLS) region because geopotential height is the independent coordinate of retrieval. Most other sounders do not offer this possibility because their independent coordinate of retrieval is pressure. To estimate the precision with which GPS radio occultation data can map winds, dry pressure profiles are simulated from the Interim European Centre for Medium-Range Weather Forecasts Re-Analysis (ERA-Interim) at the actual locations of the Challenging Minisatellite Payload (CHAMP) and the Constellation Observing System for Meteorology, Ionosphere and Climate (COSMIC) soundings for the year 2007. Monthly wind maps were created by using Bayesian interpolation on subsampled ERA-Interim data in 3–5-day bins and subsequent averaging over a month. Mapping winds in this approach requires that 1) geostrophy approximates winds; 2) dry pressure approximates pressure in the UTLS; and 3) geopotential height can be mapped accurately given sparse, nonuniform distributions of data. This study found that, under these conditions, it is possible to map monthly winds near the tropopause with an accuracy of 5.6 m s−1 with CHAMP alone and 4.5 m s−1 with COSMIC alone. The dominant contributors to uncertainty are undersampling of the atmosphere and ageostrophy, particularly at the leading and trailing edges of the subtropical jet. The former is reduced with increased density of GPS RO soundings. The latter cannot be reduced even after iteration for balanced winds. Nevertheless, it is still possible to capture the general wind pattern and to determine the position of the subtropical jet despite the uncertainty in its magnitude. COSMIC radio occultation measurements from 2006 through 2011 were used to estimate monthly geostrophic winds maps in UTLS. The resultant wind dataset is posted online.

Full access
Stephen S. Leroy, Chi O. Ao, and Olga Verkhoglyadova

Abstract

Bayesian interpolation for mapping GPS radio occultation data on a sphere is explored and its performance evaluated. Bayesian interpolation is ideally suited to the task of fitting data randomly and nonuniformly distributed with unknown error without overfitting the data. The geopotential height at dry pressure 200 hPa is simulated as data with theoretical distributions of the Challenging Mini-Satellite Payload (CHAMP) and of the Constellation Observing System for Meteorology, Ionosphere and Climate (COSMIC). The simulated CHAMP data are found to be best fit with a spherical harmonic basis of 14th degree; the COSMIC data with a spherical harmonic basis of 20th degree. The best regularizer mimics a spline fit, and relaxing the penalty for purely meridional structures or for the global mean yields little advantage. Climatologies are most accurately established by binning in ≃2-day intervals to best resolve synoptic structures in space and time. Finally, Bayesian interpolation is shown to negate a source of systematic sampling error obtained in binning and averaging highly nonuniform data but to incur another systematic error due to incomplete resolution of the background atmosphere, notably in the Southern Hemisphere.

Full access
Xuelei Feng, Feiqin Xie, Chi O. Ao, and Richard A. Anthes

Abstract

Radio occultation (RO) can provide high-vertical-resolution thermodynamic soundings of the planetary boundary layer (PBL). However, sharp moisture gradients and strong temperature inversion lead to large gradients in refractivity N and often cause ducting. Ducting results in systematically negative RO N biases resulting from a nonunique Abel inversion problem. Using 8 years (2006–13) of Constellation Observing System for Meteorology, Ionosphere and Climate (COSMIC) RO soundings and collocated European Centre for Medium-Range Weather Forecasts interim reanalysis (ERA-I) data, we confirm that the large lower-tropospheric negative N biases are mainly located in the subtropical eastern oceans and we quantify the contribution of ducting for the first time. The ducting-contributed N biases in the northeast Pacific Ocean (160°–110°W; 15°–45°N) are isolated from other sources of N biases using a two-step geometric-optics simulation. Negative bending angle biases in this region are also observed in COSMIC RO soundings. Both the negative refractivity and bending angle biases in COSMIC soundings mainly lie below ~2 km. Such bending angle biases introduce N biases that are in addition to those caused by ducting. Following the increasing PBL height from the southern California coast westward to Hawaii, centers of maxima bending angles and N biases tilt southwestward. In areas where ducting conditions prevail, ducting is the major cause of the RO N biases. Ducting-induced N biases with reference to ERA-I compose over 70% of the total negative N biases near the southern California coast, where strongest ducting conditions prevail, and decrease southwestward to less than 20% near Hawaii.

Free access
Stephen S. Leroy, Chi O. Ao, Olga P. Verkhoglyadova, and Mayra I. Oyola

Abstract

Bayesian interpolation has previously been proposed as a strategy to construct maps of radio occultation (RO) data, but that proposition did not consider the diurnal dimension of RO data. In this work, the basis functions of Bayesian interpolation are extended into the domain of the diurnal cycle, thus enabling monthly mapping of radio occultation data in synoptic time and analysis of the atmospheric tides. The basis functions are spherical harmonics multiplied by sinusoids in the diurnal cycle up to arbitrary spherical harmonic degree and diurnal cycle harmonic. Bayesian interpolation requires a regularizer to impose smoothness on the fits it produces, thereby preventing the overfitting of data. In this work, a formulation for the regularizer is proposed and the most probable values of the parameters of the regularizer determined. Special care is required when obvious gaps in the sampling of the diurnal cycle are known to occur in order to prevent the false detection of statistically significant high-degree harmonics of the diurnal cycle in the atmosphere. Finally, this work probes the ability of Bayesian interpolation to generate a valid uncertainty analysis of the fit. The postfit residuals of Bayesian interpolation are dominated not by measurement noise but by unresolved variability in the atmosphere, which is statistically nonuniform across the globe, thus violating the central assumption of Bayesian interpolation. The problem is ameliorated by constructing maps of RO data using Bayesian interpolation that partially resolve the temporal variability of the atmosphere, constructing maps for approximately every 3 days of RO data.

Restricted access
Shu-peng Ho, Xinan Yue, Zhen Zeng, Chi O. Ao, Ching-Yuang Huang, Emil R. Kursinski, and Ying-Hwa Kuo
Full access
Shu-peng Ho, Richard A. Anthes, Chi O. Ao, Sean Healy, Andras Horanyi, Douglas Hunt, Anthony J. Mannucci, Nicholas Pedatella, William J. Randel, Adrian Simmons, Andrea Steiner, Feiqin Xie, Xinan Yue, and Zhen Zeng

Abstract

Launched in 2006, the Formosa Satellite Mission 3–Constellation Observing System for Meteorology, Ionosphere and Climate (FORMOSAT-3/COSMIC) was the first constellation of microsatellites carrying global positioning system (GPS) radio occultation (RO) receivers. Radio occultation is an active remote sensing technique that provides valuable information on the vertical variations of electron density in the ionosphere, and temperature, pressure, and water vapor in the stratosphere and troposphere. COSMIC has demonstrated the great value of RO data in ionosphere, climate, and meteorological research and operational weather forecasting. However, there are still challenges using RO data, particularly in the moist lower troposphere and upper stratosphere. A COSMIC follow-on constellation, COSMIC-2, was launched into equatorial orbit in 2019. With increased signal-to-noise ratio (SNR) from improved receivers and digital beam steering antennas, COSMIC-2 will produce at least 5,000 high-quality RO profiles daily in the tropics and subtropics. In this paper, we summarize 1) recent (since 2011 when the last review was published) contributions of COSMIC and other RO observations to weather, climate, and space weather science; 2) the remaining challenges in RO applications; and 3) potential contributions to research and operations of COSMIC-2.

Free access
F. Joseph Turk, Ramon Padullés, Estel Cardellach, Chi O. Ao, Kuo-Nung Wang, David D. Morabito, Manuel de la Torre Juarez, Mayra Oyola, Svetla Hristova-Veleva, and J. David Neelin

Abstract

Observationally, a major source of uncertainty in evaluation of climate models arises from the difficulty in obtaining globally distributed, fine scale profiles of temperature, pressure and water vapor, that probe through convective precipitating clouds, from the boundary layer to the upper levels of the free troposphere. In this manuscript, a two-year analysis of data from the Radio Occultations through Heavy Precipitation (ROHP) polarimetric RO demonstration mission onboard the Spanish PAZ spacecraft is presented. ROHP measures the difference in the differential propagation phase delay (Δ𝜙) between two orthogonal polarization receive states that is induced from the presence of non-spherically shaped hydrometeors along the Global Navigation Satellite System (GNSS) propagation path, complementing the standard RO thermodynamic profile. Since Δφ is a net path-accumulated depolarization and does not resolve the precipitation structure along the propagation path, orbital coincidences between ROHP and the Global Precipitation Measurement (GPM) constellation passive MW radiometers are identified to provides three-dimensional precipitation context to the RO thermodynamic profile. Passive MW-derived precipitation profiles are used to simulate the Δφ along the ROHP propagation paths. Comparison between the simulated and observed Δφ are indicative of the ability of ROHP to detect threshold levels of ray path-averaged condensed water content, as well as to suggest possible inferences on the average ice phase hydrometeor non-sphericity. The use of the polarimetric RO vertical structure is demonstrated as a means to condition the lower tropospheric humidity by the top-most height of the associated convective cloud structure.

Open access