Abstract
Abstract Observation impact studies have received increasing amounts of research attention. The impacts of observations on numerical weather prediction (NWP) are highly dependent on assimilation algorithm, prediction system, and observation source. Therefore, the major NWP centers worldwide have each developed their own diagnostic techniques to assess observation impacts. However, similar diagnostic techniques have not yet been developed in China. In this study, a diagnostic technique was exploited with the randomized perturbation method in the Global/Regional Assimilation and Prediction System (GRAPES) 3DVAR system, and then applied to evaluate observation impacts for various regions of the world. It was found that a reasonable and stable estimation could be obtained when the number of perturbations was greater than 15. Because of differences in observations in the Northern and Southern Hemispheres, refractivity data from GNSS radio occultation (GNSS-RO), satellite radiance, and atmospheric motion vector data had more impact in the Southern Hemisphere than in the Northern Hemisphere. However, radiosonde data, aircraft, and surface data were more important in the Northern Hemisphere. Low-impact observation points were located in data-rich areas, whereas high-impact observation points were located in data-poor areas. In the equatorial region, the contributions of observations to the analysis were smaller than those in the nonequatorial regions because of the lack of proper mass–wind balance relationship. Radiosondes contributed the largest impact in China and its surrounding regions, with contributions of radiosondes and GNSS-RO data exceeding 60% of the total contributions, except for wind speed below 700 hPa.
Highlights
The objective of atmospheric data assimilation is to produce an accurate representation of the state of the atmosphere based on all existing information (Talagrand 1997)
The error variance reduction (EVR) estimation method proposed in Desroziers et al (2005) was applied in the global Global/Regional Assimilation and Prediction System (GRAPES) operational 3DVAR system to evaluate impacts of observations on the analysis results for various regions of the world
For the wind speed and temperature analysis, the percentage of total EVR was larger in the Northern Hemisphere than that in the Southern Hemisphere, because there was a greater amount of TEMP, AIREP, and SYNOP data in the Northern Hemisphere
Summary
The objective of atmospheric data assimilation is to produce an accurate representation of the state of the atmosphere based on all existing information (Talagrand 1997). The other method uses a randomization procedure whereby observations are perturbed, to estimate the error variance reduction caused by observations (Desroziers et al 2005). The OSE is the most important method for estimating the observation impacts in the operational Global/Regional Assimilation and Prediction System (GRAPES) global system. This method, requires a large amount of computational resources. The operational GRAPES three-dimensional variational (3DVAR) assimilation system and the randomization method with perturbed observations are described, which are important components of the diagnostic technique in this paper. A detailed introduction to this method with perturbed observations is provided here, including the transformation operator and terms related to error variance reduction (EVR) of state variables. A randomization method is used to solve Eqs. (4) and (5)
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