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Initial data from the Formosa Satellite-7/Constellation Observing System for Meteorology Ionosphere and Climate (FORMOSAT-7/COSMIC-2, hereafter C2), a recently launched Equatorial constellation of ...

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.

Water vapor is the most important greenhouse gas in the atmosphere with important implications not only for the Earth’s radiation and energy budget but also for various chemical, physical and dynamical processes in the stratosphere. The Constellation Observing System for Meteorology, Ionosphere and Climate (COSMIC) Radio Occultation (RO) dataset from 2007 through 2013 is used for the first time to study the distribution and variability water vapor in the upper troposphere and lower stratosphere (UTLS). The COSMIC data are compared to the Microwave Limb Sounder (MLS) data, and to two global reanalyses: The Modern-Era Retrospective analysis for Research and Application (MERRA) of the National Aeronautics and Space Administration (NASA); and, the latest reanalysis of the European Center for Medium-range Weather Forecast (ECMWF), the ERA-Interim. The MLS data have been assimilated into the MERRA, whereas the COSMIC data are used for the ERA-Interim. As a result, the MERRA agrees well with the MLS data and so does the ERA-Interim with the COSMIC data. While the monthly zonal mean distributions of water vapor from the four datasets show good agreements in northern mid-latitudes, large discrepancies exist in high southern latitudes and tropics. The MERRA shows overall a consistent seasonal cycle with MLS, but has too strong winter dehydration over the Antarctic, and is very weak in the interannual variations. The ERA-Interim fails to properly represent the winter dehydration over the Antarctic, and shows an unrealistic seasonal cycle in the tropical upper troposphere. The COSMIC data shows a good agreement with the MLS data except for the tropical "taper recorder" signal, where the COSMIC data suggest a faster upward motion than the MLS data. The COSMIC data are able to represent the moisture variabilities associated with the Quasi-Biennial Oscillation and the El Niño-Southern Oscillation.

The authors have discovered two sizeable biases in the Weather Research and Forecasting (WRF) model: a negative bias in geopotential and a warm bias in temperature, appearing both in the initial condition and the forecast. The biases increase with height and thus manifest themselves at the upper part of the model domain. Both biases stem from a common root, which is that vertical structures of specific volume and potential temperature are convex functions. The geopotential bias is caused by the particular discrete hydrostatic equation used in WRF and is proportional to the square of the thickness of model layers. For the vertical levels used in this study, the bias far exceeds the gross 1-day forecast bias combining all other sources. The bias is fixed by revising the discrete hydrostatic equation. WRF interpolates potential temperature from the grids of an external dataset to the WRF grids in generating the initial condition. Associated with the Exner function, this leads to the marked bias in temperature. By interpolating temperature to the WRF grids and then computing potential temperature, the bias is removed. The bias corrections developed in this study are expected to reduce the disparity between the forecast and observations, and eventually to improve the quality of analysis and forecast in the subsequent data assimilation. The bias corrections might be especially beneficial to assimilating height-based observations (e.g., radio occultation data).

This study uses the new satellite-based Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) mission to retrieve tropospheric profiles of temperature and moisture over the data-sparse eastern Pacific Ocean. The COSMIC retrievals, which employ a global positioning system radio occultation technique combined with “first-guess” information from numerical weather prediction model analyses, are evaluated through the diagnosis of an intense atmospheric river (AR; i.e., a narrow plume of strong water vapor flux) that devastated the Pacific Northwest with flooding rains in early November 2006. A detailed analysis of this AR is presented first using conventional datasets and highlights the fact that ARs are critical contributors to West Coast extreme precipitation and flooding events. Then, the COSMIC evaluation is provided. Offshore composite COSMIC soundings north of, within, and south of this AR exhibited vertical structures that are meteorologically consistent with satellite imagery and global reanalysis fields of this case and with earlier composite dropsonde results from other landfalling ARs. Also, a curtain of 12 offshore COSMIC soundings through the AR yielded cross-sectional thermodynamic and moisture structures that were similarly consistent, including details comparable to earlier aircraft-based dropsonde analyses. The results show that the new COSMIC retrievals, which are global (currently yielding ∼2000 soundings per day), provide high-resolution vertical-profile information beyond that found in the numerical model first-guess fields and can help monitor key lower-tropospheric mesoscale phenomena in data-sparse regions. Hence, COSMIC will likely support a wide array of applications, from physical process studies to data assimilation, numerical weather prediction, and climate research.

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] Integrated water vapor (IWV) estimates derived from four different Special Sensor Microwave Imager (SSM/I) algorithms are collocated and compared with IWV retrievals using Global Positioning System radio occultation (GPSRO) soundings from the Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) mission. The values exhibit strong overall agreement lending support for the accuracy of both the COSMIC data and the traditional passive microwave IWV products. Differences among the products varying with latitude, cloud liquid water content, rain rate, and wind speed highlight key differences between the SSM/I algorithms. Additional differences related to the coarser COSMIC spatial resolution are also observed but are independent from the other dependencies. The differences appear independent of the bottom altitude of the GPSRO soundings. The results suggest a new method of quantifying the uncertainty in individual IWV retrievals as functions of coincident environmental parameters for application to data assimilation and numerical weather prediction.