The Radio Occultation Processing Package ROPP

Culverwell, I. D.; Lewis, Huw; Offiler, D.; Marquardt, Christian; Burrows, C. P.

doi:10.5194/amtd-8-157-2015

Cited by 8 publications

(10 citation statements)

References 21 publications

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“…These simulated radiance observations are affected by the G5NR cloud field to produce observation locations, so that the selection of cloud‐free observations by the DAS is consistent with the NR synoptic state. GPS‐RO data are created using real locations of GPS‐RO, using the G5NR fields with the Radio Occultation Meteorology Satellite Application Facility software (Culverwell et al ., 2015). Full details of the observation simulation process are described in Errico et al .…”

Section: Methodsmentioning

confidence: 99%

Evaluation of adjoint‐based observation impacts as a function of forecast length using an Observing System Simulation Experiment

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Errico

Todling

et al. 2020

Quart J Royal Meteoro Soc

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Adjoints of numerical weather prediction models may be employed for forecast sensitivity to observations (FSO) in order to monitor the contribution of ingested observation data on short‐term forecast skill. However, the calculation of short‐term forecast error is difficult, due to the lack of a truly independent dataset for verification. In an Observing System Simulation Experiment (OSSE) framework, the Nature Run is able to provide a true and complete verification dataset and allows accurate evaluation of short‐term forecast errors. In this work, an OSSE developed at the National Aeronautics and Space Administration Global Modeling and Assimilation Office is used to explore the impact of observational data on forecasts in the 6–48 hour range. An adjoint of the Global Earth Observing System model is employed to compare the observation impacts estimated using both self‐analysis verification and the true Nature Run verification. Self‐analysis verification is found to inflate the estimated forecast‐error growth during the early forecast period, resulting in overestimations of observation impacts, particularly in the 6–12 hour forecast range. By 48 hours, the self‐analysis verification estimates of forecast error and observation impacts match the true values more closely. The fraction of beneficial observations is also overinflated at short forecast times when self‐analysis verification is used. The progression of impacts of an individual observation or data type depends on the character of growth of the initial‐condition error that each observation affects.

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Section: Methodsmentioning

confidence: 99%

Evaluation of adjoint‐based observation impacts as a function of forecast length using an Observing System Simulation Experiment

Privé

Errico

Todling

et al. 2020

Quart J Royal Meteoro Soc

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“…Radiance observations are generated using the Community Radiative Transfer Model (CRTM; Han et al 2006), while conventional types are generated through spatiotemporal interpolation of the NR fields. GPS-RO bending angles are generated using the operator developed by Culverwell et al (2015). Simulated errors are added to the synthetic observations in order to mimic the statistical error characteristics of real data, including both uncorrelated errors, and errors that may be correlated horizontally, vertically, or between channels, depending on the observation type.…”

Section: Methodsmentioning

confidence: 99%

Uncertainty of Observation Impact Estimation in an Adjoint Model Investigated with an Observing System Simulation Experiment

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Errico

2019

Monthly Weather Review

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Adjoint models are often used to estimate the impact of different observations on short-term forecast skill. A common difficulty with the evaluation of short-term forecast quality is the choice of verification fields. The use of self-analysis fields for verification is typical but incestuous, and it introduces uncertainty resulting from biases and errors in the analysis field. In this study, an observing system simulation experiment (OSSE) is used to explore the uncertainty in adjoint model estimations of observation impact. The availability of the true state for verification in the OSSE framework in the form of the nature run allows calculation of the observation impact without the uncertainties present in self-analysis verification. These impact estimates are compared with estimates calculated using self-analysis verification. The Global Earth Observing System, version 5 (GEOS-5), forecast model with the Gridpoint Statistical Interpolation system is used with the National Aeronautics and Space Administration Global Modeling and Assimilation Office (NASA/GMAO) OSSE capability. The adjoint model includes moist processes, with total wet energy selected as the norm for evaluation of observation impacts. The results show that there are measurable but small errors in the adjoint model estimation of observation impact as a result of self-analysis verification. In general, observations of temperature and winds tend to have overestimated impacts with self-analysis verification while observations of humidity and moisture-affected observations tend to have underestimated impacts. The small magnitude of the differences in impact estimates supports the robustness of the adjoint method of estimating observation impacts.

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“…Contrarily, if there are more measurements than the unknown profile elements, equation should be considered for atmospheric profile retrieval. Equation can be solved using the Levenberg‐Marquardt formula with fast convergence (Culverwell et al, ; Rodgers, ). However, both 14 channels for retrieving temperature profile and 8 channels for inversion of water vapor profile are much smaller than the number of the atmospheric profile layers, so the iteration equation is used to solve the minimization of J ( X ) in this study.…”

Section: Dvar Retrieval Algorithmmentioning

confidence: 99%

Retrieval of Atmospheric Profiles in the New York State Mesonet Using One‐Dimensional Variational Algorithm

Yang

Min

2018

JGR Atmospheres

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A one‐dimensional variational (1DVAR) algorithm was developed, which combines measurements taken by the ground‐based microwave radiometer profiler (MWRP) with the Rapid Refresh (RAP) model to retrieve a more accurate temperature and humidity profiles under clear sky. The passive and active microwave‐vector radiative transfer model was used to simulate brightness temperatures and calculate weighting functions for 22 channels of the MWRP. As MWRP measurements are mainly weighted in the lower atmosphere, a measurement adjustment is performed to reduce the contribution from above 10 km. The results of the 1DVAR algorithm have been compared with the MWRP built‐in neural network (NN) retrievals and radiosonde observations, which show that the 1DVAR method outperforms the NN retrieval both in temperature and water vapor. Our statistical study further shows that the water vapor profiles from RAP are biased higher than the radiosonde observations, while the 1DVAR‐retrieved humidity values show significant improvements throughout the entire 10‐km range. The improvements in temperature profiles occur mainly within the lowest 4‐km atmosphere, while upper level retrievals are mostly influenced by initial values. This is consistent with the characteristics of the Jacobian matrix and the background error covariance matrix. The outcomes of 44 clear‐sky cases show that the maximum mean retrieval error of the 1DVAR algorithm is less than 0.2 K for temperature below 4 km and less than 0.15 g/m3 in water vapor density. These results are shown to be significantly better than the NN retrieval (3.0 K and 1.25 g/m3) and also superior to the RAP reanalysis (0.3 K and 0.5 g/m3).

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The Radio Occultation Processing Package ROPP

Cited by 8 publications

References 21 publications

Evaluation of adjoint‐based observation impacts as a function of forecast length using an Observing System Simulation Experiment

Evaluation of adjoint‐based observation impacts as a function of forecast length using an Observing System Simulation Experiment

Uncertainty of Observation Impact Estimation in an Adjoint Model Investigated with an Observing System Simulation Experiment

Retrieval of Atmospheric Profiles in the New York State Mesonet Using One‐Dimensional Variational Algorithm

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