A 1DVAR retrieval applied to GMI: Algorithm description, validation, and sensitivities

Duncan, David; Kummerow, Christian D.

doi:10.1002/2016jd024808

Cited by 40 publications

(34 citation statements)

References 56 publications

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“…For the validation of derived TPW over the ocean, the TPW measured from the Advanced Microwave Scanning Radiometer 2 (AMSR2) on board the Global Change Observation Mission for Water 1 (GCOM W1) are employed. The reported accuracy of AMSR2 TPW is 1.5 kg·m −2 for RMSE when compared with TPW from the Global Positioning System [33] and 2.6 mm with radiosonde [34]. …”

Section: Validation Resultsmentioning

confidence: 99%

Atmospheric Profile Retrieval Algorithm for Next Generation Geostationary Satellite of Korea and Its Application to the Advanced Himawari Imager

2017

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Abstract:In preparation for the 2nd geostationary multi-purpose satellite of Korea with a 16-channel Advanced Meteorological Imager; an algorithm has been developed to retrieve clear-sky vertical profiles of temperature (T) and humidity (Q) based on a nonlinear optimal estimation method. The performance and characteristics of the algorithm have been evaluated using the measured data of the Advanced Himawari Imager (AHI) on board the Himawari-8 of Japan, launched in 2014. Constraints for the optimal estimation solution are provided by the forecasted T and Q profiles from a global numerical weather prediction model and their error covariance. Although the information contents for temperature is quite low due to the limited number of channels used in the retrieval; the study reveals that useful moisture information (2~3 degrees of freedom for signal) is provided from the three water vapor channels; contributing to the increase in the moisture retrieval accuracy upon the model forecast. The improvements are consistent throughout the tropospheric atmosphere with almost zero mean bias and 9% (relative humidity) of root mean square error between 100 and 1000 hPa when compared with the quality-controlled radiosonde data from 2016 August.

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Section: Validation Resultsmentioning

confidence: 99%

Atmospheric Profile Retrieval Algorithm for Next Generation Geostationary Satellite of Korea and Its Application to the Advanced Himawari Imager

2017

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“…In this context we note that the atmospheric state does not necessary need to be represented as vertical profiles where each 30 element of x is a state variable at a certain altitude or location, or represents an atmospheric layer. Alternative representations include, e.g., principal components/empirical orthogonal functions (see, e.g., Boukabara et al 2011;Munchak et al 2016;Duncan and Kummerow 2016). These can be inferred from an ensemble of spatially highly resolved prior measurements.…”

Section: Discretizationmentioning

confidence: 99%

Estimating and Reporting Uncertainties in Remotely Sensed Atmospheric Composition and Temperature

Clarmann¹,

Degenstein²,

Livesey³

et al. 2019

Preprint

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Abstract. Remote sensing of atmospheric state variables typically relies on the inverse solution of the radiative transfer equation. An adequately characterized retrieval provides information on the uncertainties of the estimated state variables as well as on how any constraint or a priori assumption affects the estimate. Reported characterization data should be intercomparable between different instruments, empirically validatable, grid-independent, usable without detailed knowledge of the instrument or retrieval technique, traceable, and still have reasonable data volume. The latter condition of adequacy may force one to work with representative rather than individual characterization data. The main sources of uncertainty are measurement noise, calibration errors, simplifications and idealizations in the radiative transfer model and retrieval scheme, auxiliary data errors, and uncertainties in atmospheric or instrumental parameters. Some of these errors affect the result in a random way, while others chiefly cause a bias or are of mixed character. Beyond this, it is of utmost importance to know the influence of any constraint and prior information on the solution. While different instruments or retrieval schemes may require different error estimation schemes, we provide a list of recommendations which shall help to unify retrieval error reporting.

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“…It flies in the Afternoon Constellation a few minutes behind OCO‐2 and thus provides a substantial number of colocated measurements. Duncan and Kummerow [] found a RMSD between AMSR2 and a radiosonde data set of 2.6mm. For this work, the Remote Sensing Systems 0.25° gridded TCWV product was used (version 7.2; Wentz et al []).…”

Section: Data Descriptionmentioning

confidence: 99%

High‐accuracy measurements of total column water vapor from the Orbiting Carbon Observatory‐2

Nelson

Crisp

Ott

et al. 2016

Geophysical Research Letters

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Accurate knowledge of the distribution of water vapor in Earth's atmosphere is of critical importance to both weather and climate studies. Here we report on measurements of total column water vapor (TCWV) from hyperspectral observations of near‐infrared reflected sunlight over land and ocean surfaces from the Orbiting Carbon Observatory‐2 (OCO‐2). These measurements are an ancillary product of the retrieval algorithm used to measure atmospheric carbon dioxide concentrations, with information coming from three highly resolved spectral bands. Comparisons to high‐accuracy validation data, including ground‐based GPS and microwave radiometer data, demonstrate that OCO‐2 TCWV measurements have maximum root‐mean‐square deviations of 0.9–1.3mm. Our results indicate that OCO‐2 is the first space‐based sensor to accurately and precisely measure the two most important greenhouse gases, water vapor and carbon dioxide, at high spatial resolution (1.3 × 2.3 km2) and that OCO‐2 TCWV measurements may be useful in improving numerical weather predictions and reanalysis products.

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A 1DVAR retrieval applied to GMI: Algorithm description, validation, and sensitivities

Cited by 40 publications

References 56 publications

Atmospheric Profile Retrieval Algorithm for Next Generation Geostationary Satellite of Korea and Its Application to the Advanced Himawari Imager

Atmospheric Profile Retrieval Algorithm for Next Generation Geostationary Satellite of Korea and Its Application to the Advanced Himawari Imager

Estimating and Reporting Uncertainties in Remotely Sensed Atmospheric Composition and Temperature

High‐accuracy measurements of total column water vapor from the Orbiting Carbon Observatory‐2

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