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Abstract
Statistical properties of observed residuals from the Mesoscale Analysis and Prediction System (MAPS), a real-time data assimilation system, were investigated. Observed residuals are defined as differences between rawinsonde observations interpolated vertically to the model levels and the predicted values from MAPS interpolated horizontally to the radiosonde locations. One-point statistical moments up to order 4 (including skewness and flatness) were computed to investigate the normality of the probability distribution of observed residuals. The finding of near-zero skewness indicates symmetry in the distribution of observed residuals, but values of flatness significantly different from 3 indicate deviations from a normal (Gaussian) distribution. These results are supported by an effective statistical test. The spatial distributions of these statistical moments show strong local variability, which is ascribed to occasional gross errors in the rawinsonde data.
The spatial correlation of observed residuals was computed for the Montgomery streamfunction and the components of the horizontal wind, following a model proposed by Roger Daley and used at the European Centre for Medium-Range Weather Forecasts. This model allows for divergence in the analyzed wind field. Complications arising from lateral boundary conditions were addressed. The spatial correlation was also computed from observed residuals of condensation pressure, which is the moisture variable in MAPS. All empirical correlations were approximated by truncated series of Bessel functions. The results are similar to those of other authors, with the exception that 3-h prediction errors in the MAPS model tend to be less geostrophic than 12-h prediction errors in global models, which have coarser resolution. The correlation range for condensation pressure was large, approaching 1000 km, reflecting the conservation of this quantity on isentropic surfaces in nonsaturated flow.
Abstract
Statistical properties of observed residuals from the Mesoscale Analysis and Prediction System (MAPS), a real-time data assimilation system, were investigated. Observed residuals are defined as differences between rawinsonde observations interpolated vertically to the model levels and the predicted values from MAPS interpolated horizontally to the radiosonde locations. One-point statistical moments up to order 4 (including skewness and flatness) were computed to investigate the normality of the probability distribution of observed residuals. The finding of near-zero skewness indicates symmetry in the distribution of observed residuals, but values of flatness significantly different from 3 indicate deviations from a normal (Gaussian) distribution. These results are supported by an effective statistical test. The spatial distributions of these statistical moments show strong local variability, which is ascribed to occasional gross errors in the rawinsonde data.
The spatial correlation of observed residuals was computed for the Montgomery streamfunction and the components of the horizontal wind, following a model proposed by Roger Daley and used at the European Centre for Medium-Range Weather Forecasts. This model allows for divergence in the analyzed wind field. Complications arising from lateral boundary conditions were addressed. The spatial correlation was also computed from observed residuals of condensation pressure, which is the moisture variable in MAPS. All empirical correlations were approximated by truncated series of Bessel functions. The results are similar to those of other authors, with the exception that 3-h prediction errors in the MAPS model tend to be less geostrophic than 12-h prediction errors in global models, which have coarser resolution. The correlation range for condensation pressure was large, approaching 1000 km, reflecting the conservation of this quantity on isentropic surfaces in nonsaturated flow.
Abstract
Global positioning system radio occultation (GPS/RO) measurements from the Challenging Minisatellite Payload (CHAMP) and Satelite de Aplicaciones Cientificas-C (SAC-C) satellites are used to improve tropospheric profile retrievals derived from the Aqua platform high-spectral-resolution Atmospheric Infrared Sounder (AIRS) and broadband Advanced Microwave Sounding Unit (AMSU) measurements under clear-sky conditions. This paper compares temperature retrievals from combined AIRS, AMSU, and CHAMP/SAC-C measurements using different techniques: 1) a principal component statistical regression using coefficients established between real (and in a few cases calculated) measurements and radiosonde atmospheric profiles; and 2) a Bayesian estimation method applied to AIRS plus AMSU temperature retrievals and GPS/RO temperature profiles. The Bayesian estimation method was also applied to GPS/RO data and the AIRS Science Team operational level-2 (version 4.0) temperature products for comparison. In this study, including GPS/RO data in the tropopause region produces the largest improvement in AIRS–AMSU temperature retrievals—about 0.5 K between 100 and 300 hPa. GPS/RO data are found to provide valuable upper-tropospheric information that improves the profile retrievals from AIRS and AMSU.
Abstract
Global positioning system radio occultation (GPS/RO) measurements from the Challenging Minisatellite Payload (CHAMP) and Satelite de Aplicaciones Cientificas-C (SAC-C) satellites are used to improve tropospheric profile retrievals derived from the Aqua platform high-spectral-resolution Atmospheric Infrared Sounder (AIRS) and broadband Advanced Microwave Sounding Unit (AMSU) measurements under clear-sky conditions. This paper compares temperature retrievals from combined AIRS, AMSU, and CHAMP/SAC-C measurements using different techniques: 1) a principal component statistical regression using coefficients established between real (and in a few cases calculated) measurements and radiosonde atmospheric profiles; and 2) a Bayesian estimation method applied to AIRS plus AMSU temperature retrievals and GPS/RO temperature profiles. The Bayesian estimation method was also applied to GPS/RO data and the AIRS Science Team operational level-2 (version 4.0) temperature products for comparison. In this study, including GPS/RO data in the tropopause region produces the largest improvement in AIRS–AMSU temperature retrievals—about 0.5 K between 100 and 300 hPa. GPS/RO data are found to provide valuable upper-tropospheric information that improves the profile retrievals from AIRS and AMSU.
Abstract
The Rapid Update Cycle (RUC), an operational regional analysis–forecast system among the suite of models at the National Centers for Environmental Prediction (NCEP), is distinctive in two primary aspects: its hourly assimilation cycle and its use of a hybrid isentropic–sigma vertical coordinate. The use of a quasi-isentropic coordinate for the analysis increment allows the influence of observations to be adaptively shaped by the potential temperature structure around the observation, while the hourly update cycle allows for a very current analysis and short-range forecast. Herein, the RUC analysis framework in the hybrid coordinate is described, and some considerations for high-frequency cycling are discussed.
A 20-km 50-level hourly version of the RUC was implemented into operations at NCEP in April 2002. This followed an initial implementation with 60-km horizontal grid spacing and a 3-h cycle in 1994 and a major upgrade including 40-km horizontal grid spacing in 1998. Verification of forecasts from the latest 20-km version is presented using rawinsonde and surface observations. These verification statistics show that the hourly RUC assimilation cycle improves short-range forecasts (compared to longer-range forecasts valid at the same time) even down to the 1-h projection.
Abstract
The Rapid Update Cycle (RUC), an operational regional analysis–forecast system among the suite of models at the National Centers for Environmental Prediction (NCEP), is distinctive in two primary aspects: its hourly assimilation cycle and its use of a hybrid isentropic–sigma vertical coordinate. The use of a quasi-isentropic coordinate for the analysis increment allows the influence of observations to be adaptively shaped by the potential temperature structure around the observation, while the hourly update cycle allows for a very current analysis and short-range forecast. Herein, the RUC analysis framework in the hybrid coordinate is described, and some considerations for high-frequency cycling are discussed.
A 20-km 50-level hourly version of the RUC was implemented into operations at NCEP in April 2002. This followed an initial implementation with 60-km horizontal grid spacing and a 3-h cycle in 1994 and a major upgrade including 40-km horizontal grid spacing in 1998. Verification of forecasts from the latest 20-km version is presented using rawinsonde and surface observations. These verification statistics show that the hourly RUC assimilation cycle improves short-range forecasts (compared to longer-range forecasts valid at the same time) even down to the 1-h projection.
