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1. Introduction A continually increasing number of meteorological observation sites is producing larger and larger amounts of data. Meteorologists can only benefit from this extensive quantity of measurements if the data quality meets the requirements implied by the intended applications. On the one hand, high quality long-term observational data are essential for identifying climate changes or for validating climate model simulations ( Feng et al. 2004 ). On the other hand, quality controlled
1. Introduction A continually increasing number of meteorological observation sites is producing larger and larger amounts of data. Meteorologists can only benefit from this extensive quantity of measurements if the data quality meets the requirements implied by the intended applications. On the one hand, high quality long-term observational data are essential for identifying climate changes or for validating climate model simulations ( Feng et al. 2004 ). On the other hand, quality controlled
). Apparently, a range of elements, including terrain, topography, and the placement of observation instruments, have made the quality control of surface observing data even more difficult. A credible quality control process is, therefore, the first step one has to take in a bid to improve surface data assimilation in numerical weather prediction. A complex quality control ( Gandin 1988 ; Collins 1998 , 2001a , b ) is applied to radiosonde height and temperature at the National Centers for Environmental
). Apparently, a range of elements, including terrain, topography, and the placement of observation instruments, have made the quality control of surface observing data even more difficult. A credible quality control process is, therefore, the first step one has to take in a bid to improve surface data assimilation in numerical weather prediction. A complex quality control ( Gandin 1988 ; Collins 1998 , 2001a , b ) is applied to radiosonde height and temperature at the National Centers for Environmental
serious errors and biases in the operational temperature and moisture satellite retrievalsproduced by statistical methods. We show ttmt similar errors and biases are found in the physical re~evalsproduced operationally since September 1988. We report experiments on quality control algorithms to dealwith the errors in the satellite data. The quality control changes resulting from this work were implemented inthe European Centre for Medium R~nge Weather Forecasts (ECMWF) system in January 1989. The
serious errors and biases in the operational temperature and moisture satellite retrievalsproduced by statistical methods. We show ttmt similar errors and biases are found in the physical re~evalsproduced operationally since September 1988. We report experiments on quality control algorithms to dealwith the errors in the satellite data. The quality control changes resulting from this work were implemented inthe European Centre for Medium R~nge Weather Forecasts (ECMWF) system in January 1989. The
implemented at least one of the approaches of FSO to compare the impact of different observing systems on modern DA systems (e.g., Zhu and Gelaro 2008 ; Cardinali 2009 ; Gelaro et al. 2010 ; Lorenc and Marriott 2014 ; Ota et al. 2013 ; Sommer and Weissmann 2014 ). Other studies have explored the applications of FSO impacts. It was shown in Lien et al. (2018) that the long-term-averaged EFSO impact provides detailed information for optimizing data selection and the design of quality control
implemented at least one of the approaches of FSO to compare the impact of different observing systems on modern DA systems (e.g., Zhu and Gelaro 2008 ; Cardinali 2009 ; Gelaro et al. 2010 ; Lorenc and Marriott 2014 ; Ota et al. 2013 ; Sommer and Weissmann 2014 ). Other studies have explored the applications of FSO impacts. It was shown in Lien et al. (2018) that the long-term-averaged EFSO impact provides detailed information for optimizing data selection and the design of quality control
; Bauer et al. 2006c ; Ding et al. 2011 ; Groff et al. 2013 , 2014 ) and the forecast model, the work on all-sky microwave radiance assimilation in the GSI analysis system has progressed at NCEP over the past several years as it has on the 3D EnVar ( Wang et al. 2013 ; Kleist and Ide 2015a ) and 4D EnVar ( Wang and Lei 2014 ; Kleist and Ide 2015b ) GSI analysis systems. To incorporate the all-sky radiances, relaxations in the criteria for data thinning and quality control of radiance data have
; Bauer et al. 2006c ; Ding et al. 2011 ; Groff et al. 2013 , 2014 ) and the forecast model, the work on all-sky microwave radiance assimilation in the GSI analysis system has progressed at NCEP over the past several years as it has on the 3D EnVar ( Wang et al. 2013 ; Kleist and Ide 2015a ) and 4D EnVar ( Wang and Lei 2014 ; Kleist and Ide 2015b ) GSI analysis systems. To incorporate the all-sky radiances, relaxations in the criteria for data thinning and quality control of radiance data have
Corporation for Atmospheric Research’s (UCAR) Constellation Observing System for Meteorology, Ionosphere and Climate (COSMIC) Data Analysis and Archival Center (CDAAC; version 002) for this study. A total of 4884 RO profiles, taken before CDAAC quality control (QC), are available during this month and are used as input into a PCA QC procedure. The optimized bending angle and refractivity ( Kuo et al. 2004 ) from these 4884 RO profiles from the surface to 40-km height are investigated. The original
Corporation for Atmospheric Research’s (UCAR) Constellation Observing System for Meteorology, Ionosphere and Climate (COSMIC) Data Analysis and Archival Center (CDAAC; version 002) for this study. A total of 4884 RO profiles, taken before CDAAC quality control (QC), are available during this month and are used as input into a PCA QC procedure. The optimized bending angle and refractivity ( Kuo et al. 2004 ) from these 4884 RO profiles from the surface to 40-km height are investigated. The original
experiments shown in this study. We think that the impact is small because this correlation-based quality control criterion may overlap with other quality control criteria, such as the 24mR criterion; that is, the precipitation data at those very bad areas could be already rejected by other criteria, so the impact is not large. 4. Results a. Global analysis and forecast errors Figure 2 shows the evolution of the global analysis RMS errors (RMSEs) of the 500-hPa u wind verified against the ERA
experiments shown in this study. We think that the impact is small because this correlation-based quality control criterion may overlap with other quality control criteria, such as the 24mR criterion; that is, the precipitation data at those very bad areas could be already rejected by other criteria, so the impact is not large. 4. Results a. Global analysis and forecast errors Figure 2 shows the evolution of the global analysis RMS errors (RMSEs) of the 500-hPa u wind verified against the ERA
states for generation of the reduced control by singular value decomposition. Another strategy studied by ( Cao et al. 2007 ; Daescu and Navon 2007 ) is based on the reduction of the model itself using EOF approach. Although the latter technique improves computational efficiency, the issue of finding an optimal low-dimensional state subspace remains an open question. This paper presents a version of the reduced control space 4DVAR data assimilation method. In contrast to previous studies (e
states for generation of the reduced control by singular value decomposition. Another strategy studied by ( Cao et al. 2007 ; Daescu and Navon 2007 ) is based on the reduction of the model itself using EOF approach. Although the latter technique improves computational efficiency, the issue of finding an optimal low-dimensional state subspace remains an open question. This paper presents a version of the reduced control space 4DVAR data assimilation method. In contrast to previous studies (e
understanding of the complex DA systems ( Zhu and Gelaro 2008 ; Cardinali 2009 ; Gelaro et al. 2010 ; Lorenc and Marriott 2014 ; Ota et al. 2013 ; Sommer and Weissmann 2014 ). Several studies have investigated the application of (generic) FSO impacts. Lien et al. (2018) demonstrated with an example of precipitation assimilation that using long-term averaged noncycled EFSO impact as guidance can accelerate the development of data selection and quality control procedures for new observing systems
understanding of the complex DA systems ( Zhu and Gelaro 2008 ; Cardinali 2009 ; Gelaro et al. 2010 ; Lorenc and Marriott 2014 ; Ota et al. 2013 ; Sommer and Weissmann 2014 ). Several studies have investigated the application of (generic) FSO impacts. Lien et al. (2018) demonstrated with an example of precipitation assimilation that using long-term averaged noncycled EFSO impact as guidance can accelerate the development of data selection and quality control procedures for new observing systems
orders of magnitude, which is usually achieved within 90 inner loops. A 6-h assimilation window, centered at the synoptic time is used for the satellite radiances. For the other data types, the assimilation window is restricted to 3 h, because the time inconsistency between the background and observation beyond 90 min from the synoptic time may introduce large errors in the innovation vector. This is especially true for wind data. After making the quality control of observations, satellite and
orders of magnitude, which is usually achieved within 90 inner loops. A 6-h assimilation window, centered at the synoptic time is used for the satellite radiances. For the other data types, the assimilation window is restricted to 3 h, because the time inconsistency between the background and observation beyond 90 min from the synoptic time may introduce large errors in the innovation vector. This is especially true for wind data. After making the quality control of observations, satellite and
