Sensitivity of Numerical Weather Prediction to the Choice of Variable for Atmospheric Moisture Analysis into the Brazilian Global Model Data Assimilation System

An efficient regional hybrid ensemble-variational (EnVar) data assimilation method using the global-ensemble-model-augmented error covariance is proposed and preliminarily tested in this study. This method uses the global ensemble error covariance as the complementary low-resolution regional ensemble error covariance. The high-resolution dynamic ensemble mean is used as the first guess in hybrid EnVar and then re-centered to the updated high-resolution dynamic ensemble perturbations after minimization analysis. In this study, the proposed method is implemented into the Weather Research and Forecasting Model’s (WRF) data assimilation system coupled with the ensemble transform Kalman filter (ETKF) and preliminarily tested for numerical weather prediction during the Mei-Yu season over eastern China. It is found that the experiment containing fewer regional dynamic ensemble members but augmented with global ensemble error covariance obtains similar results to the experiment containing many more regional dynamic ensemble members. However, the former experiment only takes up one third of the latter experiment’s computational cost. The method proposed in this study also outperforms the 3DVar, hybrid EnVar using the pure global ensemble error covariance, as well as the hybrid EnVar using regional ETKF ensemble with a smaller size. The method proposed in this paper effectively combines the contributions of the ensemble error covariance from both the global and the regional models to produce better initial conditions for the regional WRF data assimilation system.

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A land data assimilation system using the MODIS-derived land data and its application to numerical weather prediction in East Asia

Asia-Pacific Journal of Atmospheric Sciences ◽

10.1007/s13143-012-0008-4 ◽

2012 ◽

Vol 48 (1) ◽

pp. 83-95 ◽

Cited By ~ 13

Author(s):

Yoon-Jin Lim ◽

Kun-Young Byun ◽

Tae-Young Lee ◽

Hyojung Kwon ◽

Jinkyu Hong ◽

...

Keyword(s):

Data Assimilation ◽

East Asia ◽

Numerical Weather Prediction ◽

Weather Prediction ◽

Numerical Weather ◽

Land Data Assimilation ◽

Land Data Assimilation System ◽

Data Assimilation System ◽

Assimilation System

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Using Radar Wind Observations to Improve Mesoscale Numerical Weather Prediction

Weather and Forecasting ◽

10.1175/waf936.1 ◽

2006 ◽

Vol 21 (4) ◽

pp. 502-522 ◽

Cited By ~ 27

Author(s):

Qingyun Zhao ◽

John Cook ◽

Qin Xu ◽

Paul R. Harasti

Keyword(s):

High Resolution ◽

Data Assimilation ◽

Numerical Weather Prediction ◽

Weather Prediction ◽

Three Dimensional ◽

Radar Data ◽

The Impact ◽

Data Assimilation System ◽

Radar Data Assimilation ◽

Assimilation System

Abstract A high-resolution radar data assimilation system is presented for high-resolution numerical weather prediction models. The system is under development at the Naval Research Laboratory for the Navy’s Coupled Ocean–Atmosphere Mesoscale Prediction System. A variational approach is used to retrieve three-dimensional dynamical fields of atmospheric conditions from multiple-Doppler radar observations of radial velocity within a limited area. The methodology is described along with a preliminary evaluation of the impact of assimilated radar data on model forecasts using a case study of a squall line that occurred along the east coast of the United States on 9 May 2003. Results from the experiments show a significant impact from the assimilated radar radial velocity data on the model forecast of not just dynamical but also hydrological fields at all model levels for the duration of the storm. A verification system has also been developed to assess the radar data assimilation impact, and the results show improvements in the three-dimensional wind forecasts but relatively small changes in the prediction of storm locations. This study highlights the need to develop a continuous radar data assimilation system to maximize the impact of the data.

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Regional Winter Wheat Maturity Date Prediction Using Remote Sensing-Crop Model Data Assimilation and Numerical Weather Prediction

2018 7th International Conference on Agro-geoinformatics (Agro-geoinformatics) ◽

10.1109/agro-geoinformatics.2018.8476094 ◽

2018 ◽

Author(s):

Xinran Gao ◽

Jianxi Huang ◽

Hongyuan Ma ◽

Wen Zhuo ◽

Dehai Zhu

Keyword(s):

Remote Sensing ◽

Data Assimilation ◽

Winter Wheat ◽

Numerical Weather Prediction ◽

Weather Prediction ◽

Crop Model ◽

Model Data ◽

Numerical Weather ◽

Maturity Date

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Calibration and evaluation of a flood forecasting system: Utility of numerical weather prediction model, data assimilation and satellite-based rainfall

Journal of Hydrology ◽

10.1016/j.jhydrol.2015.01.042 ◽

2015 ◽

Vol 523 ◽

pp. 49-66 ◽

Cited By ~ 86

Author(s):

I. Yucel ◽

A. Onen ◽

K.K. Yilmaz ◽

D.J. Gochis

Keyword(s):

Data Assimilation ◽

Prediction Model ◽

Numerical Weather Prediction ◽

Weather Prediction ◽

Flood Forecasting ◽

Numerical Weather Prediction Model ◽

Model Data ◽

Numerical Weather ◽

Forecasting System

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Observing System Experiments with an Arctic Mesoscale Numerical Weather Prediction Model

Remote Sensing ◽

10.3390/rs11080981 ◽

2019 ◽

Vol 11 (8) ◽

pp. 981 ◽

Cited By ~ 1

Author(s):

Roger Randriamampianina ◽

Harald Schyberg ◽

Máté Mile

Keyword(s):

Data Assimilation ◽

Numerical Weather Prediction ◽

Weather Prediction ◽

The Arctic ◽

Medium Range Weather Forecast ◽

Numerical Weather ◽

Radiance Data ◽

The Impact ◽

Assimilation System

In the Arctic, weather forecasting is one element of risk mitigation, helping operators to have knowledge on weather-related risk in advance through forecasting capabilities at time ranges from a few hours to days ahead. The operational numerical weather prediction is an initial value problem where the forecast quality depends both on the quality of the forecast model itself and on the quality of the specified initial state. The initial states are regularly updated using environmental observations through data assimilation. This paper assesses the impact of observations, which are accessible through the global telecommunication and the EUMETCast dissemination systems on analyses and forecasts of an Arctic limited area AROME (Application of Research to Operations at Mesoscale) model (AROME-Arctic). An assessment through the computation of degrees of freedom for signals on the analysis, the utilization of an energy norm-based approach applied to the forecasts, verifications against observations, and a case study showed similar impacts of the studied observations on the AROME-Arctic analysis and forecast systems. The AROME-Arctic assimilation system showed a relatively high sensitivity to the humidity or humidity-sensitive observations. The more radiance data were assimilated, the lower was the estimated relative sensitivity of the assimilation system to different conventional observations. Data assimilation, at least for surface parameters, is needed to produce accurate forecasts from a few hours up to days ahead over the studied Arctic region. Upper-air conventional observations are not enough to improve the forecasting capability over the AROME-Arctic domain compared to those already produced by the ECMWF (European Centre for Medium-range Weather Forecast). Each added radiance data showed a relatively positive impact on the analyses and forecasts of the AROME-Arctic. The humidity-sensitive microwave (AMSU-B/MHS) radiances, assimilated together with the conventional observations and the Infrared Atmospheric Sounding Interferometer (IASI)-assimilated on top of conventional and microwave radiances produced enough accurate one-day-ahead forecasts of polar low.

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Use of Microwave Radiances from Metop-C and Fengyun-3 C/D Satellites for a Northern European Limited-area Data Assimilation System

Advances in Atmospheric Sciences ◽

10.1007/s00376-021-0326-5 ◽

2021 ◽

Author(s):

Magnus Lindskog ◽

Adam Dybbroe ◽

Roger Randriamampianina

Keyword(s):

Data Assimilation ◽

Positive Impact ◽

Weather Prediction ◽

Implementation Process ◽

Satellite System ◽

Limited Area ◽

Global Navigation Satellite ◽

Microwave Radiances ◽

Data Assimilation System ◽

Assimilation System

AbstractMetCoOp is a Nordic collaboration on operational Numerical Weather Prediction based on a common limited-area km-scale ensemble system. The initial states are produced using a 3-dimensional variational data assimilation scheme utilizing a large amount of observations from conventional in-situ measurements, weather radars, global navigation satellite system, advanced scatterometer data and satellite radiances from various satellite platforms. A version of the forecasting system which is aimed for future operations has been prepared for an enhanced assimilation of microwave radiances. This enhanced data assimilation system will use radiances from the Microwave Humidity Sounder, the Advanced Microwave Sounding Unit-A and the Micro-Wave Humidity Sounder-2 instruments on-board the Metop-C and Fengyun-3 C/D polar orbiting satellites. The implementation process includes channel selection, set-up of an adaptive bias correction procedure, and careful monitoring of data usage and quality control of observations. The benefit of the additional microwave observations in terms of data coverage and impact on analyses, as derived using the degree of freedom of signal approach, is demonstrated. A positive impact on forecast quality is shown, and the effect on the precipitation for a case study is examined. Finally, the role of enhanced data assimilation techniques and adaptions towards nowcasting are discussed.

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On the Impact of Unmanned Aerial System Observations on Numerical Weather Prediction in the Coastal Zone

Monthly Weather Review ◽

10.1175/mwr-d-17-0028.1 ◽

2018 ◽

Vol 146 (2) ◽

pp. 599-622 ◽

Cited By ~ 7

Author(s):

David D. Flagg ◽

James D. Doyle ◽

Teddy R. Holt ◽

Daniel P. Tyndall ◽

Clark M. Amerault ◽

...

Keyword(s):

Boundary Layer ◽

Data Assimilation ◽

Numerical Weather Prediction ◽

Research Laboratory ◽

Weather Prediction ◽

Ocean Model ◽

Naval Research ◽

Unmanned Aerial System ◽

Numerical Weather ◽

The Impact

Abstract The Trident Warrior observational field campaign conducted off the U.S. mid-Atlantic coast in July 2013 included the deployment of an unmanned aerial system (UAS) with several payloads on board for atmospheric and oceanic observation. These UAS observations, spanning seven flights over 5 days in the lowest 1550 m above mean sea level, were assimilated into a three-dimensional variational data assimilation (DA) system [the Naval Research Laboratory Atmospheric Variational Data Assimilation System (NAVDAS)] used to generate analyses for a numerical weather prediction model [the Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS)] with a coupled ocean model [the Naval Research Laboratory Navy Coastal Ocean Model (NCOM)]. The impact of the assimilated UAS observations on short-term atmospheric prediction performance is evaluated and quantified. Observations collected from 50 radiosonde launches during the campaign adjacent to the UAS flight paths serve as model forecast verification. Experiments reveal a substantial reduction of model bias in forecast temperature and moisture profiles consistently throughout the campaign period due to the assimilation of UAS observations. The model error reduction is most substantial in the vicinity of the inversion at the top of the model-estimated boundary layer. Investigations reveal a consistent improvement to prediction of the vertical position, strength, and depth of the boundary layer inversion. The relative impact of UAS observations is explored further with experiments of systematic denial of data streams from the NAVDAS DA system and removal of individual measurement sources on the UAS platform.

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Implementation of a 3-D-Var system for atmospheric profiling data assimilation into the RAMS model: initial results

Atmospheric Measurement Techniques Discussions ◽

10.5194/amtd-6-3581-2013 ◽

2013 ◽

Vol 6 (2) ◽

pp. 3581-3610

Author(s):

S. Federico

Keyword(s):

Relative Humidity ◽

Data Assimilation ◽

Initial Conditions ◽

Weather Prediction ◽

Wind Profiler ◽

Short Term ◽

Recursive Filters ◽

Data Assimilation System ◽

Rams Model ◽

Assimilation System

Abstract. This paper presents the current status of development of a three-dimensional variational data assimilation system. The system can be used with different numerical weather prediction models, but it is mainly designed to be coupled with the Regional Atmospheric Modelling System (RAMS). Analyses are given for the following parameters: zonal and meridional wind components, temperature, relative humidity, and geopotential height. Important features of the data assimilation system are the use of incremental formulation of the cost-function, and the use of an analysis space represented by recursive filters and eigenmodes of the vertical background error matrix. This matrix and the length-scale of the recursive filters are estimated by the National Meteorological Center (NMC) method. The data assimilation and forecasting system is applied to the real context of atmospheric profiling data assimilation, and in particular to the short-term wind prediction. The analyses are produced at 20 km horizontal resolution over central Europe and extend over the whole troposphere. Assimilated data are vertical soundings of wind, temperature, and relative humidity from radiosondes, and wind measurements of the European wind profiler network. Results show the validity of the analysis solutions because they are closer to the observations (lower RMSE) compared to the background (higher RMSE), and the differences of the RMSEs are consistent with the data assimilation settings. To quantify the impact of improved initial conditions on the short-term forecast, the analyses are used as initial conditions of a three-hours forecast of the RAMS model. In particular two sets of forecasts are produced: (a) the first uses the ECMWF analysis/forecast cycle as initial and boundary conditions; (b) the second uses the analyses produced by the 3-D-Var scheme as initial conditions, then is driven by the ECMWF forecast. The improvement is quantified by considering the horizontal components of the wind, which are measured at a-synoptic times by the European wind profiler network. The results show that the RMSE is effectively reduced at the short range (1–2 h). The results are in agreement with the set-up of the numerical experiment.

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