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1. Introduction In data assimilation, the process of detecting and accounting for observation errors that are statistical outliers is called quality control (QC; e.g., Daley 1991 ). An operational numerical weather prediction system may employ multiple layers of QC. For instance, observations with implausible values are usually rejected even before they enter the data assimilation process. We refer to the algorithms used for such rejection decisions as offline QC algorithms. The fact that
1. Introduction In data assimilation, the process of detecting and accounting for observation errors that are statistical outliers is called quality control (QC; e.g., Daley 1991 ). An operational numerical weather prediction system may employ multiple layers of QC. For instance, observations with implausible values are usually rejected even before they enter the data assimilation process. We refer to the algorithms used for such rejection decisions as offline QC algorithms. The fact that
for more than 75% of the time series. Other quality controls required that monthly averages at each grid cell contain at least 30 observations per month and for each time series to have standard deviation less than 25 K. GFDL model simulations of HIRS observations were subsampled using the observation times from the HIRS ascending and descending grid data. Along with these subsampled grids, monthly mean and anomaly grids were constructed using the total 3-hourly model data field. b. Simulated
for more than 75% of the time series. Other quality controls required that monthly averages at each grid cell contain at least 30 observations per month and for each time series to have standard deviation less than 25 K. GFDL model simulations of HIRS observations were subsampled using the observation times from the HIRS ascending and descending grid data. Along with these subsampled grids, monthly mean and anomaly grids were constructed using the total 3-hourly model data field. b. Simulated
pressure measured along the atmospheric vertical column. The minimum height of the vertical column considered in this process was 8 km. A total of 14, 6, and 8 radiosondes in the ABRA, GJMI, and PTVE stations, respectively, were discarded from the analysis because they either did not reach this height or presented some other type of problem during launching. Concerning the radiosonde data, rigorous quality control was carried out in order to avoid the inclusion of possible outliers in the analysis
pressure measured along the atmospheric vertical column. The minimum height of the vertical column considered in this process was 8 km. A total of 14, 6, and 8 radiosondes in the ABRA, GJMI, and PTVE stations, respectively, were discarded from the analysis because they either did not reach this height or presented some other type of problem during launching. Concerning the radiosonde data, rigorous quality control was carried out in order to avoid the inclusion of possible outliers in the analysis
zones are used if necessary. First, SAFRAN does a quality control of the observations. This is an iterative procedure based on the comparison between observed and analyzed quantities at the observation location. The analyses of temperature, humidity, wind speed, and cloudiness are performed every 6 h using all available observations (see subsection below). For this part, the first guess comes from the large-scale operational weather prediction model Arpege ( Courtier et al. 1991 ) or from the ECMWF
zones are used if necessary. First, SAFRAN does a quality control of the observations. This is an iterative procedure based on the comparison between observed and analyzed quantities at the observation location. The analyses of temperature, humidity, wind speed, and cloudiness are performed every 6 h using all available observations (see subsection below). For this part, the first guess comes from the large-scale operational weather prediction model Arpege ( Courtier et al. 1991 ) or from the ECMWF
contribution to the uncertainty on interpolated temperatures. Statistics performed in ERA-40 temperature fields show that the maximum absolute temperature gradient in the UTLS is about 10 K km −1 , which leads to a further uncertainty of 0.6 K in the interpolated temperature. The total standard deviation on temperature is therefore estimated at 0.8 K. c. Data quality control This article aims at comparing gridded reanalyses with balloon-borne observations, which is achieved through the interpolation of the
contribution to the uncertainty on interpolated temperatures. Statistics performed in ERA-40 temperature fields show that the maximum absolute temperature gradient in the UTLS is about 10 K km −1 , which leads to a further uncertainty of 0.6 K in the interpolated temperature. The total standard deviation on temperature is therefore estimated at 0.8 K. c. Data quality control This article aims at comparing gridded reanalyses with balloon-borne observations, which is achieved through the interpolation of the
with Chu et al. (2002) , we recommend not using wind data for typhoons early in the record, given the dependence on aircraft reconnaissance wind speeds, changing practices at an agency, and the potential lack of quality control in the wind data. Furthermore, pressure data in the recent JTWC record also need to be used with care given their change in WPR. Nevertheless, it appears that the historical pressure-based record of typhoon activity is more consistent between agencies than the wind
with Chu et al. (2002) , we recommend not using wind data for typhoons early in the record, given the dependence on aircraft reconnaissance wind speeds, changing practices at an agency, and the potential lack of quality control in the wind data. Furthermore, pressure data in the recent JTWC record also need to be used with care given their change in WPR. Nevertheless, it appears that the historical pressure-based record of typhoon activity is more consistent between agencies than the wind
available and hence are only used rarely in validation studies. Secondly, all data providers claim to perform quality control procedures to reduce potential errors. Therefore, and in spite of the highly varying data coverage and the uneven spatial distribution (i.e., high density at lower elevations and just few stations at higher altitude), we consider this dataset to be representative as it is the most complete and accurate, given the general data availability for each river basin. 2) SRFE This
available and hence are only used rarely in validation studies. Secondly, all data providers claim to perform quality control procedures to reduce potential errors. Therefore, and in spite of the highly varying data coverage and the uneven spatial distribution (i.e., high density at lower elevations and just few stations at higher altitude), we consider this dataset to be representative as it is the most complete and accurate, given the general data availability for each river basin. 2) SRFE This
Evaluation on the Capability of Revealing Ocean Swells from Sentinel-1A Wave Spectra Measurements.4%) (wavelength and wave direction differences are confined to 45° and 50 m, respectively). There are at most up to five partitions in one spectrum. To avoid corruption from bad measurements in the Sentinel-1A level-2 OCN partitions, the data quality control has been considered: a wind speed threshold (normally 3 m s −1 ) exists that generates enough Bragg waves to sustain σ 0 . Waves become invisible in SAR images under low wind conditions due to the low signal-to-noise ratio ( Clemente-Colón and Yan 2000
.4%) (wavelength and wave direction differences are confined to 45° and 50 m, respectively). There are at most up to five partitions in one spectrum. To avoid corruption from bad measurements in the Sentinel-1A level-2 OCN partitions, the data quality control has been considered: a wind speed threshold (normally 3 m s −1 ) exists that generates enough Bragg waves to sustain σ 0 . Waves become invisible in SAR images under low wind conditions due to the low signal-to-noise ratio ( Clemente-Colón and Yan 2000
velocity data into a series of wavenumbers from 0 to 200 at a constant radius and elevation. Based on the rotational speed of the radar antenna, the predominant oscillation periods (frequencies) can be determined by selecting the wavenumbers of crucial components. Additionally, quality control (QC) procedures are performed to mitigate the effects of the oscillations based on the FFT analysis by using a bandpass filter ( Warde and Torres 2017 ); the effects of these oscillations will be examined in
velocity data into a series of wavenumbers from 0 to 200 at a constant radius and elevation. Based on the rotational speed of the radar antenna, the predominant oscillation periods (frequencies) can be determined by selecting the wavenumbers of crucial components. Additionally, quality control (QC) procedures are performed to mitigate the effects of the oscillations based on the FFT analysis by using a bandpass filter ( Warde and Torres 2017 ); the effects of these oscillations will be examined in
An Integrated Approach to Weather Radar Calibration and Monitoring Using Ground Clutter and Satellite Comparisonsthe WSR-88D over the Melbourne, Florida, site . J. Atmos. Oceanic Technol. , 18 , 1959 – 1974 , https://doi.org/10.1175/1520-0426(2001)018<1959:CORRAR>2.0.CO;2 . 10.1175/1520-0426(2001)018<1959:CORRAR>2.0.CO;2 Marks , D. A. , D. B. Wolff , D. S. Silberstein , A. Tokay , J. L. Pippitt , and J. Wang , 2009 : Availability of high-quality TRMM ground validation data from Kwajalein, RMI: A practical application of the relative calibration adjustment technique . J. Atmos. Oceanic
the WSR-88D over the Melbourne, Florida, site . J. Atmos. Oceanic Technol. , 18 , 1959 – 1974 , https://doi.org/10.1175/1520-0426(2001)018<1959:CORRAR>2.0.CO;2 . 10.1175/1520-0426(2001)018<1959:CORRAR>2.0.CO;2 Marks , D. A. , D. B. Wolff , D. S. Silberstein , A. Tokay , J. L. Pippitt , and J. Wang , 2009 : Availability of high-quality TRMM ground validation data from Kwajalein, RMI: A practical application of the relative calibration adjustment technique . J. Atmos. Oceanic
