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N. C. Privé
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N. C. Privé and R. M. Errico

Abstract

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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N. C. Privé and R. M. Errico

Abstract

General circulation models can now be run at very high spatial resolutions to capture finescale features, but saving the full-spatial-resolution output at every model time step is usually not practical because of storage limitations. To reduce storage requirements, the model output may be produced at reduced temporal and/or spatial resolutions. When this reduced-resolution output is then used in situations where spatiotemporal interpolation is required, such as the generation of synthetic observations for observing system simulation experiments, interpolation errors can significantly affect the quality and usefulness of the reduced-resolution model output. Although it is common in practice to record model output at the highest possible spatial resolution with relatively infrequent temporal output, this may not be the best option to minimize interpolation errors. In this study, two examples using a high-resolution global run of the Goddard Earth Observing System Model, version 5 (GEOS-5), are presented to illustrate cases in which the optimal output dataset configurations for interpolation have high temporal frequency but reduced spatial resolutions. Interpolation errors of tropospheric temperature, specific humidity, and wind fields are investigated. The relationship between spatial and temporal output resolutions and interpolation errors is also characterized for the example model.

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N. C. Privé, R. M. Errico, and K.-S. Tai

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Most rawinsondes are launched once or twice daily, at 0000 and/or 1200 UTC; only a small number of the total rawinsonde observations are taken at 0600 and 1800 UTC (“off hour” cycle times). In this study, the variations of forecast and analysis quality between cycle times and the potential improvement of skill due to supplemental rawinsonde measurements at 0600 and 1800 UTC are tested in the framework of an observing system simulation experiment (OSSE). The National Aeronautics and Space Administration Global Modeling and Assimilation Office (NASA GMAO) Goddard Earth Observing System Model, version 5 (GEOS-5), is used with the GMAO OSSE setup for an experiment emulating the months of July and August with the 2011 observational network. The OSSE is run with and without supplemental rawinsonde observations at 0600 and 1800 UTC, and the differences in analysis error and forecast skill are quantified. The addition of supplemental rawinsonde observations results in significant improvement of analysis quality in the Northern Hemisphere for both the 0000/1200 and 0600/1800 UTC cycle times, with greater improvement for the off-hour times. Reduction of root-mean-square errors on the order of 1%3% for wind and temperature is found at the 24- and 48-h forecast times. There is a slight improvement in Northern Hemisphere anomaly correlations at the 120-h forecast time.

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N. C. Privé, Yuanfu Xie, Steven Koch, Robert Atlas, Sharanya J. Majumdar, and Ross N. Hoffman

Abstract

High-altitude, long-endurance unmanned aircraft systems (HALE UAS) are capable of extended flights for atmospheric sampling. A case study was conducted to evaluate the potential impact of dropwindsonde observations from HALE UAS on tropical cyclone track prediction; tropical cyclone intensity was not addressed. This study employs a global observing system simulation experiment (OSSE) developed at the National Oceanic and Atmospheric Administration/Earth System Research Laboratory (NOAA/ESRL) that is based on the NOAA/National Centers for Environmental Prediction gridpoint statistical interpolation (GSI) data assimilation system and Global Forecast System (GFS) model. Different strategies for dropwindsonde deployment and UAS flight paths were compared. The introduction of UAS-deployed dropwindsondes was found to consistently improve the track forecast skill during the early forecast up to 96 h, with the caveat that the experiments omitted both vortex relocation and dropwindsondes from manned flights in the tropical cyclone region. The more effective UAS dropwindsonde deployment patterns sampled both the environment and the body of the tropical cyclone.

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