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In the Global Positioning System (GPS) radio occultation (RO) technique, the inverse Abel transform of measured bending angle (Abel inversion, hereafter AI) is the standard means of deriving the refractivity. While concise and straightforward to apply, the AI accumulates and propagates the measurement error downward. The measurement error propagation is detrimental to the refractivity in lower altitudes. In particular, it builds up negative refractivity bias in the tropical lower troposphere. An alternative to AI is the numerical inversion of the forward Abel transform, which does not incur the integration of error-possessing measurement and thus precludes the error propagation. The variational regularization (VR) proposed in this study approximates the inversion of the forward Abel transform by an optimization problem in which the regularized solution describes the measurement as closely as possible within the measurement's considered accuracy. The optimization problem is then solved iteratively by means of the adjoint technique. VR is formulated with error covariance matrices, which permit a rigorous incorporation of prior information on measurement error characteristics and the solution's desired behavior into the regularization. VR holds the control variable in the measurement space to take advantage of the posterior height determination and to negate the measurement error due to the mismodeling of the refractional radius. The advantages of having the solution and the measurement in the same space are elaborated using a purposely corrupted synthetic sounding with a known true solution. The competency of VR relative to AI is validated with a large number of actual RO soundings. The comparison to nearby radiosonde observations shows that VR attains considerably smaller random and systematic errors compared to AI. A noteworthy finding is that in the heights and areas that the measurement bias is supposedly small, VR follows AI very closely in the mean refractivity deserting the first guess. In the lowest few kilometers that AI produces large negative refractivity bias, VR reduces the refractivity bias substantially with the aid of the background, which in this study is the operational forecasts of the European Centre for Medium-Range Weather Forecasts (ECMWF). It is concluded based on the results presented in this study that VR offers a definite advantage over AI in the quality of refractivity.

The impact of global positioning system (GPS) radio occultation (RO) data on an intense synoptic-scale storm that occurred over the Southern Ocean in December 2007 is evaluated, and a synoptic explanation of the assessed impact is offered. The impact is assessed by using the three-dimensional variational data assimilation scheme (3DVAR) of the Weather Research and Forecasting (WRF) Model Data Assimilation system (WRFDA), and by comparing two experiments: one with and the other without assimilating the refractivity data from four different RO missions. Verifications indicate significant positive impacts of the RO data in various measures and parameters as well as in the track and intensity of the Antarctic cyclone. The analysis of the atmospheric processes underlying the impact shows that the assimilation of the RO data yields substantial improvements in the large-scale circulations that in turn control the development of the Antarctic storm. For instance, the RO data enhanced the strength of a 500-hPa trough over the Southern Ocean and prevented the katabatic flow near the coast of East Antarctica from an overintensification. This greatly influenced two low pressure systems of a comparable intensity, which later merged together and evolved into the major storm. The dominance of one low over the other in the merger dramatically changed the track, intensity, and structure of the merged storm. The assimilation of GPS RO data swapped the dominant low, leading to a remarkable improvement in the subsequent storm’s prediction.

Liposomes, which can deliver payload at target site, have been studied as drug carrier. However, conventional liposomes have limitation for drug release at target site. Therefore, we developed hydroxyapatite (HA) coated ultrasound sen- sitive liposomes to increase drug release at target site and to enhance stability in blood stream. Control liposome was prepared using hydrogenated soy phosphatidylcholine (HSPC) and cholesterol, and then we assessed HA coating on the surface of con- trol liposomes using calcium acetate, phosphoric acid, and 25% ammonium solution. Doxorubicin was used as a model drug. Size of HA coated liposomes was 120 nm and encapsulation efficiency of doxorubicin in liposomes was up to 95%. Size of HA coated liposomes are not changed in 30% serum solution, however, the control liposomes was 1.4 fold increased. After ultrasound triggered drug release from liposomes, intracellular efficiency of drug released from HA coated liposomes was 3 fold increased compared to control liposomes. In this study, we developed ultrasound sensitive liposomes to enhance drug release, which will be applied in controlled drug release at disease site.

As the understanding of our Earth system grows, the importance of comprehending the structure and processes in the remote stratosphere is intensified and the interest in stratospheric observations mushrooms. Despite its great potential, radio occultation (RO) data have been underused in exploiting the stratosphere. A major reason for the underutilization is the imperfections in preexisting RO data processing methods. We propose an advanced stratospheric RO data processing, where the variational method provides a general framework in which multiple-frequency RO measurements of different quality are effectively combined with the aid of a priori. The variational combination (VAR) is designed to extract the most information from RO measurements, where a priori plays a role of enhancing the observation and attenuating measurement noise. The signal-to-noise ratio (SNR) is found to be a universal quality indicator, which concisely describes the uncertainty of RO measurements in diverse conditions. The measured SNR is used to parameterize a dynamic observation error, which is essential for the VAR to use the observation optimally. Tests with real data show that VAR significantly improves the accuracy of the RO retrieval even in the upper stratosphere, where the RO data were once considered to possess little observational value. When compared with independent radiosonde observations, for instance, the VAR-produced data are more accurate than the analysis from the European Center for Medium-Range Weather Forecasts for which the radiosonde data have been assimilated. The VAR-produced data are also precise enough to reveal the systematic error of the radiosonde data.

In this study, the GPS radio occultation (RO) data from the Challenging Minisatellite Payload (CHAMP) and Satellite de Aplicaciones Cientificas-C (SAC-C) missions are assimilated. An updated version of the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) four-dimensional variational data assimilation system (4DVAR) is used to assess the impact of the GPS RO data on analyses and short-range forecasts over the Antarctic. The study was performed during the period of intense cyclonic activity in the Ross Sea, 9–19 December 2001. On average 66 GPS RO soundings were assimilated daily. For the assimilation over a single 12-h period, the impact of GPS RO data was only marginally positive or near neutral, and it varied markedly from one 12-h period to another. The large case-to-case variation was attributed to the low number of GPS RO soundings and a strong dependency of forecast impact on the location of the soundings relative to the rapidly developing cyclone. Despite the moderate general impact, noticeable reduction of temperature error in the upper troposphere and lower stratosphere was found, which demonstrates the value of GPS RO data in better characterizing the tropopause. Significant error reduction was also noted in geopotential height and wind fields in the stratosphere. Those improvements indicate that early detection of the upper-level precursors for storm development is a potential benefit of GPS RO data. When the assimilation period was extended to 48 h, a considerable positive impact of GPS RO data was found. All parameters that were investigated (i.e., temperature, pressure, and specific humidity) showed the positive impact throughout the entire model atmosphere for forecasts extending up to 5 days. The impact increased in proportion to the length of the assimilation period. Although the differences in the analyses as a result of GPS RO assimilation were relatively small initially, the subtle change and subsequent nonlinear growth led to noticeable forecast improvements at longer ranges. Consequently, the positive impact of GPS RO data was more evident in longer-range (e.g., greater than 2 days) forecasts. A correlation coefficient is introduced to quantify the linear relationship between the analysis errors without GPS RO assimilation and the analysis increments induced by GPS RO assimilation. This measure shows that the growth of GPS RO–induced modifications over time is related to the prominent error reduction observed in GPS RO experiments. The measure may also be useful for understanding how cycling analysis accumulates the positive impact of GPS RO data for an extended period of assimilation.

The radio occultation (RO) technique, which makes use of radio signals transmitted by the global positioning system (GPS) satellites, has emerged as a powerful and relatively inexpensive approach for sounding the global atmosphere with high precision, accuracy, and vertical resolution in all weather and over both land and ocean. On 15 April 2006, the joint Taiwan-U.S. Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC)/Formosa Satellite Mission 3 (COSMIC/FORMOSAT-3, hereafter COSMIC) mission, a constellation of six microsatellites, was launched into a 512-km orbit. After launch the satellites were gradually deployed to their final orbits at 800 km, a process that took about 17 months. During the early weeks of the deployment, the satellites were spaced closely, offering a unique opportunity to verify the high precision of RO measurements. As of September 2007, COSMIC is providing about 2000 RO soundings per day to support the research and operational communities. COSMIC RO dat...

In this study, a digital filter is introduced into the fifth-generation Pennsylvania State University‐National Center for Atmospheric Research Mesoscale Model (MM5) four-dimensional variational data assimilation (4DVAR) system as a weak constraint to control high-frequency oscillations, which negatively affect assimilation performance. To assess the impact of the digital filter and to understand how the digital-filter 4DVAR functions, a series of observing system simulation experiments are conducted with the assimilation of global positioning system (GPS) refractivity soundings for a cyclogenesis case over the Antarctic region. It is shown that the use of a digital filter, centered at the midpoint of the assimilation period, is effective in suppressing the highfrequency waves. The imbalance during the early period of assimilation is further reduced by utilizing an additional short-span filter, starting at the beginning of the assimilation period. The filtering of the wind field is found to be the most effective in suppressing high-frequency oscillations. It is also revealed that the imposed weak constraint significantly reduces the wave-reflection problem caused by imperfect upper boundary conditions. It is concluded that the weakly constrained 4DVAR with digital filters not only reduces dynamic imbalance, but also significantly improves the qualities of analysis and forecast. Without projecting its solution onto the highfrequency waves, which diminish rapidly with forecast time, the constrained 4DVAR is able to yield additional improvement in the model initial condition in the larger-scale range and hence utilizes the available observations more effectively when compared with the unconstrained 4DVAR.

In this paper, we describe the GPS radio occultation (RO) inversion process currently used at the University Corporation for Atmospheric Research (UCAR) COSMIC (Constellation Observing System for Meteorology, Ionosphere and Climate) Data Analysis and Archive Center (CDAAC). We then evaluate the accuracy of RO refractivity soundings of the CHAMP (CHAllenging Minisatellite Payload) and SACC (Satellite de Aplicaciones Cientificas-C) missions processed by CDAAC software, using data primarily from the month of December 2001. Our results show that RO soundings have the highest accuracy from about 5 km to 25 km. In this region of the atmosphere, the observational errors (which include both measurement and representativeness errors) are generally in the range of 0.3% to 0.5% in refractivity. The observational errors in the tropical lower troposphere increase toward the surface, and reach @3% in the bottom few kilometers of the atmosphere. The RO observational errors also increase above 25 km, particularly over the higher latitudes of the winter hemisphere. These error estimates are, in general, larger than earlier theoretical predictions. The larger observational errors in the lower tropical troposphere are attributed to the complicated structure of humidity, superrefraction and receiver tracking errors. The larger errors above 25 km are related to observational noise (mainly, uncalibrated ionospheric effects) and the use of ancillary data for noise reduction through an optimization procedure. We demonstrate that RO errors above 25 km can be substantially reduced by selecting only low-noise occultations. Our results show that RO soundings have smaller observational errors of refractivity than radiosondes when compared to analyses and short-term forecasts, even in the tropical lower troposphere. This difference is most likely related to the larger representativeness errors associated with the radiosonde, which provides in situ (point) measurements. The RO observational errors are found to be comparable with or smaller than 12-hour forecast errors of the NCEP (National Centers for Environmental Prediction) Aviation (AVN) model, except in the tropical lower troposphere below 3 km. This suggests that RO observations will improve global weather analysis and prediction. It is anticipated that with the use of an advanced signal tracking technique (open-loop tracking) in future missions, such as COSMIC, the accuracy of RO soundings can be further improved.

Radio occultation (RO) is a promising source of observation for weather and climate applications. However, the uncertainties arising from imperfect retrieval algorithms may weaken the overall confidence in the data and discourage their use. As an alternative approach of assessing the quality of RO data while avoiding the nuisance of retrieval errors, this study proposes to use minimally processed data (measurement) instead of derived RO data. This study compares measured phase paths with their model counterparts, simulated with an effective ray tracer for which the refractive indices along the complete ray path linking the transmitter and the receiver are realistically specified. The comparison of phase measurements with the European Centre for Medium-Range Weather Forecasts (ECMWF) data made in the observation space shows that the RO measurements are of sufficient accuracy to uncover regional-scale systematic errors in ECMWF's operational analysis and the 45-year reanalysis (ERA40), and to clearly depict the error growth of short-term ERA40 forecasts. In the southern hemispheric stratosphere, in particular, the RO measurements served as a robust reference against which both of the two analyses were significantly biased in opposite directions even though they were produced by the same center using virtually the same set of data. The measurement and ECMWF analyses showed a close agreement in the standard deviation except for the regions and heights that the quality of the ECMWF data is controversial. This confirms the high precision of RO measurements and also indicates that the main problem of the ECMWF analyses lies in their systematic error.

[1] A two-dimensional curved ray tracer (CRT) is developed to study the propagation path of radio signals across a heterogeneous planetary atmosphere. The method, designed to achieve improvements in both computational efficiency and accuracy over conventional straight-line methods, takes rays' first-order bending into account to better describe curved raypaths in the stratified atmosphere. CRT is then used to simulate the phase path from GPS radio occultation (RO). The merit of the ray tracing approach in GPS RO is explicit consideration of horizontal variation in the atmosphere, which may lead to a sizable error but is disregarded in traditional retrieval schemes. In addition, direct modeling of the phase path takes advantage of simple error characteristics in the measurement. With provision of ionospheric and neutral atmospheric refractive indices, in this effort, rays are traced along the full range of GPS-low Earth orbiting (LEO) radio links just as the measurements are made in real life. Here, ray shooting is employed to realize the observed radio links with controlled accuracy. CRT largely reproduces the very measured characteristics of GPS signals. When compared, the measured and simulated phases show remarkable agreement. The cross validation between CRT and GPS RO has confirmed not only the strength of CRT but also the high accuracy of GPS RO measurements. The primary motivation for this study is enabling effective quality control for GPS RO data, overcoming a complicated error structure in the high-level data. CRT has also shown a great deal of potential for improved utilization of GPS RO data for geophysical research.

[1] One of the fundamental difficulties that arise when using GPS Radio Occultation (RO) data in exploiting the stratosphere is that the air becomes rarefied with increasing height and accentuates the ionospheric effect and noise contained in the measurement. Customarily, the conventional linear combination (CLC) is used to extract neutral atmospheric components from dual-frequency (L1 and L2) RO data. The CLC combines and magnifies measurement noises, and thus works well only for those measurements of low noise. Although the L1 data are of considerably higher quality than the L2 data, the CLC does not take this into account and treats both equally; this makes the CLC-produced data less attractive. The authors propose a new approach, named Noise-Aware Combination (NAC), which is a generalized combination that factors in the presence of measurement noise. In this NAC method, the L1 and L2 data are each regarded independently, with each contributing to the combination according to its dynamically assessed accuracy. The performance of both the CLC and NAC are tested with two sets of data: one of synthetic data and the other of real data. The tests confirm that the NAC yields significant error reductions when compared to the CLC. While the noise in the CLC-produced data stands out in high altitudes and compels the data to be blended with the a priori, the NAC relies far less on this blending. The clear advantage of the NAC over the CLC would greatly enhance the value of RO for climate research.