The COSMIC radio occultation mission represents a revolution in atmospheric sounding from space, with precise, accurate, and all-weather global observations useful for weather, climate, and space weather research and operations. GPS Signal GPS Satellite

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 SAC-C (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.

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.

The Arctic is a vital component of the global climate, and its rapid environmental evolution is an important element of climate change around the world. To detect and diagnose the changes occurring to the coupled Arctic climate system, a state-of-the-art synthesis for assessment and monitoring is imperative. This paper presents the Arctic System Reanalysis, version 2 (ASRv2), a multiagency, university-led retrospective analysis (reanalysis) of the greater Arctic region using blends of the polar-optimized version of the Weather Research and Forecasting (Polar WRF) Model and WRF three-dimensional variational data assimilated observations for a comprehensive integration of the regional climate of the Arctic for 2000–12. New features in ASRv2 compared to version 1 (ASRv1) include 1) higher-resolution depiction in space (15-km horizontal resolution), 2) updated model physics including subgrid-scale cloud fraction interaction with radiation, and 3) a dual outer-loop routine for more accurate data assimilation. ASRv2 surface and pressure-level products are available at 3-hourly and monthly mean time scales at the National Center for Atmospheric Research (NCAR). Analysis of ASRv2 reveals superior reproduction of near-surface and tropospheric variables. Broadscale analysis of forecast precipitation and site-specific comparisons of downward radiative fluxes demonstrate significant improvement over ASRv1. The high-resolution topography and land surface, including weekly updated vegetation and realistic sea ice fraction, sea ice thickness, and snow-cover depth on sea ice, resolve finescale processes such as topographically forced winds. Thus, ASRv2 permits a reconstruction of the rapid change in the Arctic since the beginning of the twenty-first century–complementing global reanalyses. ASRv2 products will be useful for environmental models, verification of regional processes, or siting of future observation networks.

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 six satellites carrying advanced radio occultation receivers, exhibit high signal‐to‐noise ratio, precision, and accuracy, and the ability to provide high vertical resolution profiles of bending angles and refractivity, which contain information on temperature and water vapor in the challenging tropical atmosphere. After an initial calibration/validation phase, over 100,000 soundings of bending angles and refractivity that passed quality control in October 2019 are compared with independent data, including radiosondes, model forecasts, and analyses. The comparisons show that C2 data meet expectations of high accuracy, precision, and capability to detect superrefraction. When fully operational, the C2 satellites are expected to produce ~5,000 soundings per day, providing freely available observations that will enable improved forecasts of weather, including tropical cyclones, and weather, space weather, and climate research.

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.

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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.