Retrieving clear‐sky atmospheric parameters from SEVIRI and ABI infrared radiances

Jin, Xin; Li, Jun; Schmit, Timothy J.; Li, Jinlong; Goldberg, Mitchell D.; Gurka, James J.

doi:10.1029/2008jd010040

Cited by 34 publications

(57 citation statements)

References 37 publications

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“…As can be seen in the figures, retrieved TPW agrees well with the AMSR2 TPW for both summer and winter with smaller RMSE in winter than in moist summer. Compared to the previous study that evaluated the retrieved TPW from SEVIRI [4] and GOES-13 sounder [6] using the AMSR-Earth Observing System (AMSR-E) TPW over the ocean in August, the results in this study show a slightly smaller bias (0.1 mm) and RMSE (2.28 mm) and this result meets the requirements of GK-2A TPW products, which is 1 mm for mean bias and 3 mm for RMSE. …”

supporting

confidence: 47%

“…The layer mean bias and RMSE between 100 and 1000 hPa is −0.0% and 9.0% for the retrieval and 1.9% and 9.8% for the forecast, respectively. Previous studies showed improvements in the retrieved relative humidity profile RMSE over the ECMWF forecast [4] or the NCEP GFS forecast [6] between 300 and 700 hPa in particular, while in this study the largest improvement over the forecast is seen in the upper atmosphere (above 300 hPa) than in the mid-atmosphere and this may require further investigation. …”

Section: Retrieved Profilesmentioning

confidence: 45%

“…Previous studies with SEVIRI and simulated ABI [4] or GOES-13 Sounder [6] using a physical retrieval algorithm also showed little improvement in the retrieved temperature profile over the forecast. This is simply because NWP temperature forecasts are already in good accuracy and thus the retrieval algorithms are hard to improve the temperature profile over the forecast with just one sounding channel, i.e., the CO 2 absorption channel (13.3 µm) of AHI or ABI.…”

Section: Retrieved Profilesmentioning

confidence: 95%

“…Reference candidates for bias correction include radiances from a well-calibrated satellite instrument, NWP model background or analysis, collocated radiosonde and model fields generated near radiosonde stations [14]. In a previous study that applied the clear-sky ABI legacy profile retrieval algorithm to the SEVIRI measurements, Jin et al [4] used quality-controlled radiosonde data for the bias correction of SEVIRI data and obtained significantly improved moisture retrieval accuracy in the upper atmospheric layers (600~300 hPa). In this study, two different reference data is used for a rough estimate of AHI bias on land and the ocean: the collocated one-month radiosonde (RAOB) data from June 2016 for the bias correction on land and the collocated 6-hourly ECMWF Reanalysis (ERA) data (00, 06, 12 and 18 UTC) from 1 to 10 June 2016 over the ocean.…”

Section: Satellite Measurementsmentioning

confidence: 99%

“…Retrieval simulations and theoretical analysis [3] showed that ABI data, in conjunction with NWP model forecast information, may produce additional moisture information that can improve forecast and will be able to adequately substitute legacy sounder-based products. Jin et al [4] also showed that useful water vapor information in the upper layer (higher than 700 hPa) can be derived from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) and simulated ABI data with high spatial and temporal resolution using the ABI Legacy Atmospheric Profile (LAP) retrieval algorithm, which was originally developed for GOES Sounder products [5] based on an iterative physical retrieval scheme. Lee et al [6] applied the same retrieval scheme to the GOES-13 sounder measurements and evaluated the moisture-related products such as total precipitable water (TPW) and lifted index including vertical profiles of temperature and moisture.…”

Section: Introductionmentioning

confidence: 99%

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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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supporting

confidence: 47%

Section: Retrieved Profilesmentioning

confidence: 45%

Section: Retrieved Profilesmentioning

confidence: 95%

Section: Satellite Measurementsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

2017

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Geostationary satellite‐based 6.7 μm band best water vapor information layer analysis over the Tibetan Plateau

et al. 2016

JGR Atmospheres

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The best water vapor information layer (BWIL) of the 6.7 μm water vapor absorption infrared (IR) band for the FengYun‐2E is investigated over the Tibetan Plateau with standard atmospheric profile and European Centre for Medium‐Range Weather Forecasts (ECMWF) operational model analysis data. The sensitivity tests show that surface characteristics over the Tibetan Plateau have a significant influence on the BWIL. To be specific, topographic elevation, colder skin temperature, and lower emissivity tend to lift the altitude of the BWIL, decrease its magnitude, and narrow the half‐width range. The results from statistical analysis indicate that the altitude of the BWIL reaches the highest in summer and the lowest in winter. Meanwhile, the altitude of the BWIL is highly correlated with the water vapor amount above 500 hPa over the Tibetan Plateau and above 300 hPa over the East China Plain, respectively. The diurnal variation in the BWIL is synchronous with the diurnal variation in the surface skin temperature. It can be concluded from the study that surface characteristics over high terrain in dry and cold atmospheres have more significant impacts on the BWIL. With multiple water vapor absorption IR bands, the imagers on board the new generation of geostationary satellites will provide crucial improvement in water vapor remote sensing over the current single water vapor band on board the FY‐2 series according to the analysis in this study.

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Techniques and challenges in the assimilation of atmospheric water observations for numerical weather prediction towards convective scales

Bannister

Chipilski

Martínez‐Alvarado

2019

Quart J Royal Meteoro Soc

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While contemporary numerical weather prediction models represent the large‐scale structure of moist atmospheric processes reasonably well, they often struggle to maintain accurate forecasts of small‐scale features such as convective rainfall. Even though high‐resolution models resolve more of the flow, and are therefore arguably more accurate, moist convective flow becomes increasingly nonlinear and dynamically unstable. Importantly, the models' initial conditions are typically sub‐optimal, leaving scope to improve the accuracy of forecasts with improved data assimilation. To address issues regarding the use of atmospheric water‐related observations – especially at convective scales (also known as storm scales) – this article discusses the observation and assimilation of water‐related quantities. Special emphasis is placed on background error statistics for variational and hybrid methods which need special attention for water variables. The challenges of convective‐scale data assimilation of atmospheric water information are discussed, which are more difficult to tackle than at larger scales. Some of the most important challenges include the greater degree of inhomogeneity and lower degree of smoothness of the flow, the high volume of water‐related observations (e.g. from radar, microwave and infrared instruments), the need to analyse a range of hydrometeors, the increasing importance of position errors in forecasts, the greater sophistication of forward models to allow use of indirect observations (e.g. cloud‐ and precipitation‐affected observations), the need to account for the flow‐dependent multivariate “balance” between atmospheric water and both dynamical and mass fields, and the inherent non‐Gaussian nature of atmospheric water variables.

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Retrieving clear‐sky atmospheric parameters from SEVIRI and ABI infrared radiances

Cited by 34 publications

References 37 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

Geostationary satellite‐based 6.7 μm band best water vapor information layer analysis over the Tibetan Plateau

Techniques and challenges in the assimilation of atmospheric water observations for numerical weather prediction towards convective scales

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