Tae-Kwon Wee

THE COSMIC/FORMOSAT-3 MISSION : Early Results

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...

Diagnosis of an intense atmospheric river impacting the pacific northwest: Storm summary and offshore vertical structure observed with COSMIC satellite retrievals

Abstract 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 satellit...

Impact of a digital filter as a weak constraint in MM5 4DVAR: An observing system simulation experiment

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.

Assimilation of GPS radio occultation refractivity data from CHAMP and SAC-C missions over high southern latitudes with MM5 4DVAR

Abstract 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 de...

An Impact Assessment of GPS Radio Occultation Data on Prediction of a Rapidly Developing Cyclone over the Southern Ocean

AbstractThe 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...

Two Overlooked Biases of the Advanced Research WRF (ARW) Model in Geopotential Height and Temperature

The authors have discovered two sizeable biases in the Weather Research and Forecasting (WRF) model: a negativebias in geopotential and a warm bias in temperature, appearingboth in the initial conditionand 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 levelsusedinthisstudy,thebiasfar exceedsthegross1-dayforecastbiascombiningallothersources.Thebias 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 reducethe disparity betweenthe forecast and observations, andeventuallyto improvethe quality ofanalysisandforecastinthesubsequentdataassimilation.Thebiascorrectionsmightbeespeciallybeneficial to assimilating height-based observations (e.g., radio occultation data).

Advanced stratospheric data processing of radio occultation with a variational combination for multifrequency GNSS signals

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.

The Arctic System Reanalysis, Version 2

AbstractThe 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 assimi...