An Invitation to Meteorological Data Assimilation

2016 ◽  
pp. 165-192
Author(s):  
Ágnes Bodó ◽  
Petra Csomós
Keyword(s):  
Data Assimilation ◽  
Meteorological Data

scholarly journals Evaluation of Regional Aircraft Observations Using TAMDAR

2010 ◽  
Vol 25 (2) ◽  
pp. 627-645 ◽  
Author(s):  
William R. Moninger ◽  
Stanley G. Benjamin ◽  
Brian D. Jamison ◽  
Thomas W. Schlatter ◽  
Tracy Lorraine Smith ◽  
...  
Keyword(s):  
Relative Humidity ◽  
Data Assimilation ◽  
Meteorological Data ◽  
Time Range ◽  
Observation System ◽  
Vertical Resolution ◽  
Commercial Aircraft ◽  
Aircraft Observation ◽  
Aircraft Observations ◽  
High Vertical Resolution

Abstract A multiyear evaluation of a regional aircraft observation system [Tropospheric Aircraft Meteorological Data Reports (TAMDAR)] is presented. TAMDAR observation errors are compared with errors in traditional reports from commercial aircraft [aircraft meteorological data reports (AMDAR)], and the impacts of TAMDAR observations on forecasts from the Rapid Update Cycle (RUC) over a 3-yr period are evaluated. Because of the high vertical resolution of TAMDAR observations near the surface, a novel verification system has been developed and employed that compares RUC forecasts against raobs every 10 hPa; this revealed TAMDAR-related positive impacts on RUC forecasts—particularly for relative humidity forecasts—that were not evident when only raob mandatory levels were considered. In addition, multiple retrospective experiments were performed over two 10-day periods, one in winter and one in summer; these allowed for the assessment of the impacts of various data assimilation strategies and varying data resolutions. TAMDAR’s impacts on 3-h RUC forecasts of temperature, relative humidity, and wind are found to be positive and, for temperature and relative humidity, substantial in the region, altitude, and time range over which TAMDAR-equipped aircraft operated during the studied period of analysis.

scholarly journals A study on WRF radar data assimilation for hydrological rainfall prediction

2013 ◽  
Vol 17 (8) ◽  
pp. 3095-3110 ◽  
Author(s):  
J. Liu ◽  
M. Bray ◽  
D. Han
Keyword(s):  
Data Assimilation ◽  
Real Time ◽  
Meteorological Data ◽  
Weather Radar ◽  
Radar Data ◽  
Rain Gauge ◽  
Radar Reflectivity ◽  
Storm Event ◽  
Hydrological Applications ◽  
Radar Data Assimilation

Abstract. Mesoscale numerical weather prediction (NWP) models are gaining more attention in providing high-resolution rainfall forecasts at the catchment scale for real-time flood forecasting. The model accuracy is however negatively affected by the "spin-up" effect and errors in the initial and lateral boundary conditions. Synoptic studies in the meteorological area have shown that the assimilation of operational observations, especially the weather radar data, can improve the reliability of the rainfall forecasts from the NWP models. This study aims at investigating the potential of radar data assimilation in improving the NWP rainfall forecasts that have direct benefits for hydrological applications. The Weather Research and Forecasting (WRF) model is adopted to generate 10 km rainfall forecasts for a 24 h storm event in the Brue catchment (135.2 km2) located in southwest England. Radar reflectivity from the lowest scan elevation of a C-band weather radar is assimilated by using the three-dimensional variational (3D-Var) data-assimilation technique. Considering the unsatisfactory quality of radar data compared to the rain gauge observations, the radar data are assimilated in both the original form and an improved form based on a real-time correction ratio developed according to the rain gauge observations. Traditional meteorological observations including the surface and upper-air measurements of pressure, temperature, humidity and wind speed are also assimilated as a bench mark to better evaluate and test the potential of radar data assimilation. Four modes of data assimilation are thus carried out on different types/combinations of observations: (1) traditional meteorological data; (2) radar reflectivity; (3) corrected radar reflectivity; (4) a combination of the original reflectivity and meteorological data; and (5) a combination of the corrected reflectivity and meteorological data. The WRF rainfall forecasts before and after different modes of data assimilation are evaluated by examining the rainfall temporal variations and total amounts which have direct impacts on rainfall–runoff transformation in hydrological applications. It is found that by solely assimilating radar data, the improvement of rainfall forecasts are not as obvious as assimilating meteorological data; whereas the positive effect of radar data can be seen when combined with the traditional meteorological data, which leads to the best rainfall forecasts among the five modes. To further improve the effect of radar data assimilation, limitations of the radar correction ratio developed in this study are discussed and suggestions are made on more efficient utilisation of radar data in NWP data assimilation.

The effect of satellite and conventional meteorological data assimilation on the mesoscale modeling of monsoon depressions over India

2008 ◽  
Vol 101 (1-2) ◽  
pp. 65-92 ◽  
Author(s):  
V. F. Xavier ◽  
A. Chandrasekar ◽  
H. Rahman ◽  
D. Niyogi ◽  
K. Alapaty
Keyword(s):  
Data Assimilation ◽  
Meteorological Data ◽  
Mesoscale Modeling ◽  
Monsoon Depressions

scholarly journals AVALIAÇÃO DO WRF-4DVAR EM UM CASO DE EVENTO SEVERO NO RIO GRANDE DO SUL

2016 ◽  
Vol 38 (2) ◽  
pp. 1077
Author(s):  
Luana Ribeiro Macedo ◽  
João Luiz Martins Basso ◽  
Yoshihiro Yamasaki
Keyword(s):  
Data Assimilation ◽  
Meteorological Data ◽  
Rio Grande ◽  
Real Data ◽  
Radar Reflectivity ◽  
Rio Grande Do Sul ◽  
Mesoscale System ◽  
Accumulated Rainfall ◽  
Reflectivity Data ◽  
The Impact

The WRF mesoscale system 4DVAR data assimilation technique have been used with the purpose of evaluating the impact of the meteorological data assimilation on the numeric time prognosis over the Rio Grande do Sul state. It has been done utilizing the surface and altitude data. The consistency analysis has been done evaluating the numerical prognosis exploring the differences between the analysis with and without data assimilation. The produced prognosis results have been compared spatially using the TRMM satellite data as well as the Canguçu radar reflectivity data. The accumulated rainfall has been validated and compared spatially with the TRMM data for the time period of 12 hours comprehended between October 29th and 30th of 2014. It was possible to realize that as well as the WRF, the WRFVAR overestimated the rainfall values. The radar reflectivity field without data assimilation for October 30th at 06:00UTC detected most accurately the reflectivity centers over the state. On the other hand this field with data assimilation did not present good skill. The temperature field analyses reveal that the 4DVAR assimilation system contributes, one way or another, presenting a little improvement for some points compared to the real data.

scholarly journals The impacts of uncertainties in emissions on aerosol data assimilation and short-term PM<sub>2.5</sub> predictions in CMAQ v5.2.1 over East Asia

2020 ◽  
Author(s):  
Sojin Lee ◽  
Chul Han Song ◽  
Kyung Man Han ◽  
Daven K. Henze ◽  
Kyunghwa Lee ◽  
...  
Keyword(s):  
Air Quality ◽  
Data Assimilation ◽  
East Asia ◽  
Conventional Method ◽  
Meteorological Data ◽  
Three Dimensional ◽  
Short Term ◽  
Background Error ◽  
Multi Scale ◽  
Error Covariance

Abstract. For the purpose of improving PM prediction skills in East Asia, we estimated a new background error covariance matrix (BEC) for aerosol data assimilation using surface PM2.5 observations that accounts for the uncertainties in anthropogenic emissions. In contrast to the conventional method to estimate the BEC that uses perturbations in meteorological data, this method additionally considered the perturbations using two different emission inventories. The impacts of the new BEC were then tested for the prediction of surface PM2.5 over East Asia using Community Multi-scale Air Quality (CMAQ) initialized by three-dimensional variational method (3D-VAR). The surface PM2.5 data measured at 154 sites in South Korea and 1,535 sites in China were assimilated every six hours during the Korea-United States Air Quality Study (KORUS-AQ) campaign period (1 May–14 June 2016). Data assimilation with our new BEC showed better agreement with the surface PM2.5 observations than that with the conventional method. Our method also showed closer agreement with the observations in 24-hour PM2.5 predictions with ~ 44 % fewer negative biases than the conventional method. We conclude that increased standard deviations, together with horizontal and vertical length scales in the new BEC, tend to improve the data assimilation and short-term predictions for the surface PM2.5. This paper also suggests further research efforts devoted to estimating the BEC to improve PM2.5 predictions.

scholarly journals Interchannel Error Correlation Associated with AIRS Radiance Observations: Inference and Impact in Data Assimilation

2007 ◽  
Vol 46 (6) ◽  
pp. 714-725 ◽  
Author(s):  
Louis Garand ◽  
Sylvain Heilliette ◽  
Mark Buehner
Keyword(s):  
Data Assimilation ◽  
Meteorological Data ◽  
Specific Humidity ◽  
Observation Error ◽  
Carbon Dioxide Absorption ◽  
Background Error ◽  
Error Matrix ◽  
Vapor Sensing ◽  
Error Correlation ◽  
The Impact

Abstract The interchannel observation error correlation (IOEC) associated with radiance observations is currently assumed to be zero in meteorological data assimilation systems. This assumption may lead to suboptimal analyses. Here, the IOEC is inferred for the Atmospheric Infrared Radiance Sounder (AIRS) hyperspectral radiance observations using a subset of 123 channels covering the spectral range of 4.1–15.3 μm. Observed minus calculated radiances are computed for a 1-week period using a 6-h forecast as atmospheric background state. A well-established technique is used to separate the observation and background error components for each individual channel and each channel pair. The large number of collocations combined with the 40-km horizontal spacing between AIRS fields of view allows robust results to be obtained. The resulting background errors are in good agreement with those inferred from the background error matrix used operationally in data assimilation at the Meteorological Service of Canada. The IOEC is in general high among the water vapor–sensing channels in the 6.2–7.2-μm region and among surface-sensitive channels. In contrast, it is negligible for channels within the main carbon dioxide absorption band (13.2–15.4 μm). The impact of incorporating the IOEC is evaluated from 1D variational retrievals at 381 clear-sky oceanic locations. Temperature increments differ on average by 0.25 K, and ln(q) increments by 0.10, where q is specific humidity. Without IOEC, the weight given to the observations appears to be too high; the assimilation attempts to fit the observations nearly perfectly. The IOEC better constrains the variational assimilation process, and the rate of convergence is systematically faster by a factor of 2.

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