Variational bias correction of satellite radiance data in the ERA-Interim reanalysis

Abstract This study describes the initial application of radiance bias correction and channel selection in the hourly updated Rapid Refresh model. For this initial application, data from the Atmospheric Infrared Sounder (AIRS) are used; this dataset gives atmospheric temperature and water vapor information at higher vertical resolution and accuracy than previously launched low-spectral resolution satellite systems. In this preliminary study, data from AIRS are shown to add skill to short-range weather forecasts over a relatively data-rich area. Two 1-month retrospective runs were conducted to evaluate the impact of assimilating clear-sky AIRS radiance data on 1–12-h forecasts using a research version of the National Oceanic and Atmospheric Administration (NOAA) Rapid Refresh (RAP) regional mesoscale model already assimilating conventional and other radiance [AMSU-A, Microwave Humidity Sounder (MHS), HIRS-4] data. Prior to performing the assimilation, a channel selection and bias-correction spinup procedure was conducted that was appropriate for the RAP configuration. RAP forecasts initialized from analyses with and without AIRS data were verified against radiosonde, surface atmosphere, precipitation, and satellite radiance observations. Results show that the impact from AIRS radiance data on short-range forecast skill in the RAP system is small but positive and statistically significant at the 95% confidence level. The RAP-specific channel selection and bias correction procedures described in this study were the basis for similar applications to other radiance datasets now assimilated in version 3 of RAP implemented at NOAA’s National Centers for Environmental Prediction (NCEP) in August 2016.

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Impact of Radiance Data Assimilation on the Prediction of Heavy Rainfall in RMAPS: A Case Study

Remote Sensing ◽

10.3390/rs10091380 ◽

2018 ◽

Vol 10 (9) ◽

pp. 1380 ◽

Cited By ~ 4

Author(s):

Yanhui Xie ◽

Jiancheng Shi ◽

Shuiyong Fan ◽

Min Chen ◽

Youjun Dou ◽

...

Keyword(s):

Data Assimilation ◽

Heavy Rainfall ◽

Precipitation Forecast ◽

Observation Data ◽

Rainfall Events ◽

Satellite Radiance Data ◽

Radiance Data ◽

The Impact ◽

Heavy Rainfall Events

Herein, a case study on the impact of assimilating satellite radiance observation data into the rapid-refresh multi-scale analysis and prediction system (RMAPS) is presented. This case study targeted the 48 h period from 19–20 July 2016, which was characterized by the passage of a low pressure system that produced heavy rainfall over North China. Two experiments were performed and 24 h forecasts were produced every 3 h. The results indicated that the forecast prior to the satellite radiance data assimilation could not accurately predict heavy rainfall events over Beijing and the surrounding area. The assimilation of satellite radiance data from the advanced microwave sounding unit-A (AMSU-A) and microwave humidity sounding (MHS) improved the skills of the quantitative precipitation forecast to a certain extent. In comparison with the control experiment that only assimilated conventional observations, the experiment with the integrated satellite radiance data improved the rainfall forecast accuracy for 6 h accumulated precipitation after about 6 h, especially for rainfall amounts that were greater than 25 mm. The average rainfall score was improved by 14.2% for the 25 mm threshold and by 35.8% for 50 mm of rainfall. The results also indicated a positive impact of assimilating satellite radiances, which was primarily reflected by the improved performance of quantitative precipitation forecasting and higher spatial correlation in the forecast range of 6–12 h. Satellite radiance observations provided certain valuable information that was related to the temperature profile, which increased the scope of the prediction of heavy rainfall and led to an improvement in the rainfall scoring in the RMAPS. The inclusion of satellite radiance observations was found to have a small but beneficial impact on the prediction of heavy rainfall events as it relates to our case study conditions. These findings suggest that the assimilation of satellite radiance data in the RMAPS can provide an overall improvement in heavy rainfall forecasting.

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Impact of Satellite Radiance Data on Simulations of Bay of Bengal Tropical Cyclones Using the WRF-3DVAR Modeling System

IEEE Transactions on Geoscience and Remote Sensing ◽

10.1109/tgrs.2015.2498971 ◽

2016 ◽

Vol 54 (4) ◽

pp. 2285-2303 ◽

Cited By ~ 34

Author(s):

Ashish Routray ◽

U. C. Mohanty ◽

Krishna K. Osuri ◽

S. C. Kar ◽

Dev Niyogi

Keyword(s):

Tropical Cyclones ◽

Bay Of Bengal ◽

Satellite Radiance Data ◽

Radiance Data

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Issues Regarding the Assimilation of Cloud and Precipitation Data

Journal of the Atmospheric Sciences ◽

10.1175/2006jas2044.1 ◽

2007 ◽

Vol 64 (11) ◽

pp. 3785-3798 ◽

Cited By ~ 94

Author(s):

Ronald M. Errico ◽

Peter Bauer ◽

Jean-François Mahfouf

Keyword(s):

Weather Prediction ◽

Difficult Problem ◽

Careful Examination ◽

Weather Forecasts ◽

Clear Sky ◽

Precipitation Characteristics ◽

Satellite Radiance Data ◽

Prediction Systems ◽

Radiance Data ◽

Peculiar Nature

Abstract The assimilation of observations indicative of quantitative cloud and precipitation characteristics is desirable for improving weather forecasts. For many fundamental reasons, it is a more difficult problem than the assimilation of conventional or clear-sky satellite radiance data. These reasons include concerns regarding nonlinearity of the required observation operators (forward models), nonnormality and large variances of representativeness, retrieval, or observation–operator errors, validation using new measures, dynamic and thermodynamic balances, and possibly limited predictability. Some operational weather prediction systems already assimilate precipitation observations, but much more research and development remains. The apparently critical, fundamental, and peculiar nature of many issues regarding cloud and precipitation assimilation implies that their more careful examination will be required for accelerating progress.

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