Applications of satellite radiance data in the medium range forecasting environment at ECMWF

1992 ◽  
Vol 12 (7) ◽  
pp. 303-312
Author(s):  
William A. Heckley ◽  
Jean-Jaques Morcrette ◽  
Ernst Klinker
Keyword(s):  
Medium Range ◽  
Satellite Radiance Data ◽  
Radiance Data

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

2018 ◽  
Vol 10 (9) ◽  
pp. 1380 ◽  
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.

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

2016 ◽  
Vol 54 (4) ◽  
pp. 2285-2303 ◽  
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

scholarly journals Issues Regarding the Assimilation of Cloud and Precipitation Data

2007 ◽  
Vol 64 (11) ◽  
pp. 3785-3798 ◽  
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.

Direct assimilation of satellite radiance data in GRAPES variational assimilation system

2008 ◽  
Vol 53 (22) ◽  
pp. 3465-3469 ◽  
Author(s):  
GuoFu Zhu ◽  
JiShan Xue ◽  
Hua Zhang ◽  
ZhiQuan Liu ◽  
ShiYu Zhuang ◽  
...  
Keyword(s):  
Variational Assimilation ◽  
Satellite Radiance Data ◽  
Radiance Data ◽  
Assimilation System

scholarly journals Satellite radiance data assimilation for rainfall prediction in Java Region

2017 ◽  
Vol 54 ◽  
pp. 012037 ◽  
Author(s):  
Novvria Sagita ◽  
Rini Hidayati ◽  
Rahmat Hidayat ◽  
Indra Gustari
Keyword(s):  
Data Assimilation ◽  
Rainfall Prediction ◽  
Satellite Radiance Data ◽  
Radiance Data

Satellite Radiance Data Assimilation within the Hourly Updated Rapid Refresh

2017 ◽  
Vol 32 (4) ◽  
pp. 1273-1287 ◽  
Author(s):  
Haidao Lin ◽  
Stephen S. Weygandt ◽  
Stanley G. Benjamin ◽  
Ming Hu
Keyword(s):  
Model Systems ◽  
Limited Area ◽  
Weather Model ◽  
Standard Procedures ◽  
Analysis Package ◽  
Satellite Radiance Data ◽  
Data Coverage ◽  
Environmental Prediction ◽  
Radiance Data

Abstract Assimilation of satellite radiance data in limited-area, rapidly updating weather model/assimilation systems poses unique challenges compared to those for global model systems. Principal among these is the severe data restriction posed by the short data cutoff time. Also, the limited extent of the model domain reduces the spatial extent of satellite data coverage and the lower model top of regional models reduces the spectral usage of radiance data especially for infrared data. These three factors impact the quality of the feedback to the bias correction procedures, making the procedures potentially less effective. Within the National Oceanic and Atmospheric Administration (NOAA) Rapid Refresh (RAP) hourly updating prediction system, satellite radiance data are assimilated using the standard procedures within the Gridpoint Statistical Interpolation (GSI) analysis package. Experiments for optimizing the operational implementation of radiance data into RAP and for improving benefits of radiance data have been performed. The radiance data impact for short-range forecasts has been documented to be consistent and statistically significantly positive in systematic RAP retrospective runs using real-time datasets. The radiance data impact has also been compared with conventional observation datasets within RAP. The configuration for RAP satellite radiance assimilation evaluated here is that implemented at the National Centers for Environmental Prediction (NCEP) in August 2016.

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