scholarly journals Assimilating visible satellite reflectances in combination with radar data in a pre-operational convective-scale seamless prediction system 

2021 ◽  
Author(s):  
Liselotte Bach ◽  
Thomas Deppisch ◽  
Leonhard Scheck ◽  
Alberto de Lozar ◽  
Christian Welzbacher ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Bias Correction ◽  
Weather Prediction ◽  
Radar Data ◽  
Observation Error ◽  
Major Step ◽  
Observation Systems ◽  
Seamless Transition ◽  
Convective Scale ◽  
Nwp Model

<p>In the framework of the SINFONY project at Deutscher Wetterdienst (DWD) we have developed data assimilation of visible satellite reflectances of the SEVIRI instrument (MSG) and radar observations in a rapid update cycle (ICON-D2-KENDA-RUC) which will be running in a first 24/7-testsuite starting in spring of this year. Our major goal related to the assimilation of these new observation systems is to improve the positioning of cloud and precipitation systems and their intensities, needed for the seamless transition of radar nowcasting to numerical weather prediction (NWP) in our SINFONY system. We give an overview of the steps undertaken in the course of developing the data assimilation of visible satellite reflectances. This includes quality control, observation error modelling, data reduction and bias correction of the reflectances. Further development and enhancement of the forward operator MFASIS is still ongoing. A major step to allow for a successful assimilation has been the improvement of microphysical consistency between the NWP model and MFASIS both with 1-moment and 2-moment microphysics to reduce the bias of first-guess departures. To further enhance and stabilize the agreement of observations and model climatologies over the course of the year and different weather regimes, an innovative histogram-based bias correction has been developed. We show results of data assimilation experiments combining visible reflectances and radar data in the ICON-D2-KENDA-Rapid Update Cycle using 2-moment microphysics. Further, we discuss the improvement of forecast skill from both observing systems and the way they complement each other – putting special emphasis to the key variable of interest in the SINFONY system, namely radar reflectivity.</p>

scholarly journals Toward a variational assimilation of polarimetric radar observations in a convective-scale numerical weather prediction (NWP) model

2020 ◽  
Vol 13 (5) ◽  
pp. 2279-2298
Author(s):  
Guillaume Thomas ◽  
Jean-François Mahfouf ◽  
Thibaut Montmerle
Keyword(s):  
Data Assimilation ◽  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Numerical Weather ◽  
Observation Operator ◽  
Weather Radars ◽  
Convective Scale ◽  
The Difference ◽  
Input Variables ◽  
Nwp Model

Abstract. This paper presents the potential of nonlinear and linear versions of an observation operator for simulating polarimetric variables observed by weather radars. These variables, deduced from the horizontally and vertically polarized backscattered radiations, give information about the shape, the phase and the distributions of hydrometeors. Different studies in observation space are presented as a first step toward their inclusion in a variational data assimilation context, which is not treated here. Input variables are prognostic variables forecasted by the AROME-France numerical weather prediction (NWP) model at convective scale, including liquid and solid hydrometeor contents. A nonlinear observation operator, based on the T-matrix method, allows us to simulate the horizontal and the vertical reflectivities (ZHH and ZVV), the differential reflectivity ZDR, the specific differential phase KDP and the co-polar correlation coefficient ρHV. To assess the uncertainty of such simulations, perturbations have been applied to input parameters of the operator, such as dielectric constant, shape and orientation of the scatterers. Statistics of innovations, defined by the difference between simulated and observed values, are then performed. After some specific filtering procedures, shapes close to a Gaussian distribution have been found for both reflectivities and for ZDR, contrary to KDP and ρHV. A linearized version of this observation operator has been obtained by its Jacobian matrix estimated with the finite difference method. This step allows us to study the sensitivity of polarimetric variables to hydrometeor content perturbations, in the model geometry as well as in the radar one. The polarimetric variables ZHH and ZDR appear to be good candidates for hydrometeor initialization, while KDP seems to be useful only for rain contents. Due to the weak sensitivity of ρHV, its use in data assimilation is expected to be very challenging.

scholarly journals A Case Study on the Impact of Ensemble Data Assimilation with GNSS-Zenith Total Delay and Radar Data on Heavy Rainfall Prediction

2020 ◽  
Vol 148 (3) ◽  
pp. 1075-1098 ◽  
Author(s):  
Shu-Chih Yang ◽  
Zih-Mao Huang ◽  
Ching-Yuang Huang ◽  
Chih-Chien Tsai ◽  
Ta-Kang Yeh
Keyword(s):  
Data Assimilation ◽  
Heavy Rainfall ◽  
Weather Prediction ◽  
Radar Data ◽  
Total Delay ◽  
Ensemble Data Assimilation ◽  
Zenith Total Delay ◽  
Ensemble Data ◽  
Convective Scale ◽  
The Impact

Abstract The performance of a numerical weather prediction model using convective-scale ensemble data assimilation with ground-based global navigation satellite systems-zenith total delay (ZTD) and radar data is investigated on a heavy rainfall event that occurred in Taiwan on 10 June 2012. The assimilation of ZTD and/or radar data is performed using the framework of the WRF local ensemble transform Kalman filter with a model grid spacing of 2 km. Assimilating radar data is beneficial for predicting the rainfall intensity of this local event but produces overprediction in southern Taiwan and underprediction in central Taiwan during the first 3 h. Both errors are largely overcome by assimilating ZTD data to improve mesoconvective-scale moisture analyses. Consequently, assimilating both the ZTD and radar data show advantages in terms of the location and intensity of the heavy rainfall. Sensitivity experiments involving this event indicate that the impact of ZTD data is improved by using a broader horizontal localization scale than the convective scale used for radar data assimilation. This optimization is necessary in order to consider more fully the network density of the ZTD observations and the horizontal scale of the moisture transport by the southwesterly flow in this case.

scholarly journals Evaluating the use of Aeolus satellite observations in the regional NWP model Harmonie-Arome

2021 ◽  
Author(s):  
Susanna Hagelin ◽  
Roohollah Azad ◽  
Magnus Lindskog ◽  
Harald Schyberg ◽  
Heiner Körnich
Keyword(s):  
Data Assimilation ◽  
Weather Prediction ◽  
Line Of Sight ◽  
Limited Area ◽  
Horizontal Line ◽  
Observation Error ◽  
Potential Improvement ◽  
Nwp Model ◽  
The Impact

Abstract. The impact of using wind speed data from the Aeolus satellite in a limited area Numerical Weather Prediction (NWP) system is being investigated using the limited area NWP model Harmonie-Arome over the Nordic region. We assimilate the Horizontal Line of Sight (HLOS) winds observed by Aeolus using 3D-Var data assimilation for two different periods, one in Sept–Oct 2018 when the satellite was recently launched, and a later period in Apr–May 2020 to investigate the updated data processing of the HLOS winds. We find that the quality of the Aeolus observations have degraded between the first and second experiment period over our domain. However observations from Aeolus, in particular the Mie winds, have a clear impact on the analysis of the NWP model for both periods whereas the forecast impact is neutral when compared against radiosondes. Results from evaluation of observation minus background and observation minus analysis departures based on Desroziers diagnostics show that the observation error should be increased for Aeolus data in our experiments, but the impact of doing so is small. We also see that there is potential improvement in using 4D-Var data assimilation, which generate flow-dependent analysis increments, with the Aeolus data.

scholarly journals Evaluating the use of Aeolus satellite observations in the regional numerical weather prediction (NWP) model Harmonie–Arome

2021 ◽  
Vol 14 (9) ◽  
pp. 5925-5938
Author(s):  
Susanna Hagelin ◽  
Roohollah Azad ◽  
Magnus Lindskog ◽  
Harald Schyberg ◽  
Heiner Körnich
Keyword(s):  
Data Assimilation ◽  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Limited Area ◽  
Horizontal Line ◽  
Observation Error ◽  
Numerical Weather ◽  
Potential Improvement ◽  
Nwp Model ◽  
The Impact

Abstract. The impact of using wind observations from the Aeolus satellite in a limited-area numerical weather prediction (NWP) system is being investigated using the limited-area NWP model Harmonie–Arome over the Nordic region. We assimilate the horizontal line-of-sight (HLOS) winds observed by Aeolus using 3D-Var data assimilation for two different periods, one in September–October 2018 when the satellite was recently launched and a later period in April–May 2020 to investigate the updated data processing of the HLOS winds. We find that the quality of the Aeolus observations has degraded between the first and second experiment period over our domain. However, observations from Aeolus, in particular the Mie winds, have a clear impact on the analysis of the NWP model for both periods, whereas the forecast impact is neutral when compared against radiosondes. Results from evaluation of observation minus background and observation minus analysis departures based on Desroziers diagnostics show that the observation error should be increased for Aeolus data in our experiments, but the impact of doing so is small. We also see that there is potential improvement in using 4D-Var data assimilation, which generates flow-dependent analysis increments, with the Aeolus data.

scholarly journals Toward a variational assimilation of polarimetric radar observation in a convective scale NWP model

2019 ◽  
Author(s):  
Guillaume Thomas ◽  
Jean-François Mahfouf ◽  
Thibaut Montmerle
Keyword(s):  
Data Assimilation ◽  
Weather Prediction ◽  
Observation Operator ◽  
Weather Radars ◽  
Non Linear ◽  
Convective Scale ◽  
The Difference ◽  
Input Variables ◽  
Nwp Model ◽  
Differential Reflectivity

Abstract. This paper presents the potential of non-linear and linear versions of an observation operator for simulating polarimetric variables observed by weather radars. These variables, deduced from the horizontally and vertically polarised backscattered radiations, give information about the shape, the phase and the distributions of hydrometeors. Different studies in observation space are presented, as a first step toward their inclusion in a variational data assimilation context, which is not treated here. Input variables are prognostic variables forecasted by the AROME-France Numerical Weather Prediction (NWP) model at convective scale, including liquid and solid hydrometeor contents. A non-linear observation operator, based on the T-matrix method, allows to simulate the horizontal and the vertical reflectivities (ZHH and ZVV), the differential reflectivity ZDR, the specific differential phase KDP and the copolar correlation coefficient ρHV. To assess the uncertainty of such simulations, perturbations have been applied on input parameters of the operator, such as dielectric constant, shape and orientation of the scatterers. Statistics of innovations, defined by the difference between simulated and observed values, are then performed. After some specific filtering procedures, shapes close to Gaussian have been found for both reflectivities and for ZDR, contrarily to KDP and ρHV. A linearised version of this observation operator has been obtained by its Jacobian matrix estimated with the finite difference method. This step allows to study the sensitivity of polarimetric variables to hydrometeor content perturbations, in the model geometry as well as in the radar one. The polarimetric variables ZHH and ZDR appear to be good candidates for hydrometeor initialisation, while KDP seems to be useful only for rain contents. Due to the weak sensitivity of ρHV, its use in data assimilation is expected to be very challenging.

An idealized Testbed for Radar Data Assimilation

2020 ◽  
Author(s):  
Yuefei Zeng ◽  
Tijana Janjic ◽  
Alberto de Lozar ◽  
Ulrich Blahak ◽  
Axel Seifert
Keyword(s):  
Data Assimilation ◽  
Initial Conditions ◽  
Numerical Models ◽  
Radar Data ◽  
Observation Error ◽  
Supercell Storm ◽  
Highly Nonlinear ◽  
Convective Scale ◽  
Radar Data Assimilation ◽  
Made In

<p> </p> <pre class="moz-quote-pre">Data assimilation on the convective scale uses high-resolution numerical models of the atmosphere that resolve highly nonlinear dynamics and physics. These non-hydrostatic, convection permitting models are in short runs very sensitive to proper initial conditions. <br />However, the estimation of initial conditions is hampered by assumptions made in data assimilation algorithms <br />and in their models of the observation error and model error uncertainty. Within this work, an idealized testbed <br />for Radar Data Assimilation has been developed, which uses Kilometre-scale ENsemble Data Assimilation (KENDA) system <br />of the (Deutscher Wetterdienst) DWD. A series of data assimilation experiments for a supercell storm are conducted. <br />The sensitivity to the configurations of the radar forward operator and specification of the observation error <br />is investigated. Moreover, impacts of different observations (radial wind, reflectivity or both) <br />on the performance of data assimilation cycles and 6-h forecasts are shown, for instance, <br />the preservation of divergence, vorticity and mass of hydrometeors, compared to the nature run is of special interest.</pre> <p> </p>

Evaluating the use of Aeolus satellite observations in a regional NWP model over the Nordic countries

2021 ◽  
Author(s):  
Susanna Hagelin ◽  
Roohollah Azad ◽  
Magnus Lindskog ◽  
Harald Schyberg ◽  
Heiner Körnich
Keyword(s):  
Data Assimilation ◽  
Weather Prediction ◽  
Line Of Sight ◽  
Limited Area ◽  
Horizontal Line ◽  
Observation Error ◽  
Potential Improvement ◽  
Nwp Model ◽  
The Impact

<p>The impact of using wind speed data from the Aeolus satellite in a limited area Numerical Weather Prediction (NWP) system is being investigated using the limited area NWP model Harmonie-Arome over the Nordic region. We assimilate the Horizontal Line of Sight (HLOS) winds observed by Aeolus using a 3D-Var data assimilation for two different periods, one in Sept-Oct 2018 when the satellite was recently launched, and a later period in Apr-May 2020 to investigate the updated data processing of the HLOS winds. We find that the quality of the Aeolus observations have degraded between the first and second experiment period over our domain. However observations from Aeolus, in particular the Mie winds, have a clear impact on the analysis of the NWP model for both periods whereas the forecast impact is neutral when compared against radiosondes. Results from evaluation of observation minus background and observation minus analysis departures based on  Desroziers diagnostics show that the observation error should be increased for Aeolus data in our experiments, but the impact of doing so is small. We also see that there is potential improvement in using 4D-Var data assimilation with the Aeolus data. </p>

scholarly journals Cloud and Precipitation Parameterizations in Modeling and Variational Data Assimilation: A Review

2007 ◽  
Vol 64 (11) ◽  
pp. 3766-3784 ◽  
Author(s):  
Philippe Lopez
Keyword(s):  
Data Assimilation ◽  
General Circulation ◽  
Large Scale ◽  
Weather Prediction ◽  
General Circulation Models ◽  
Variational Data Assimilation ◽  
Current Status ◽  
General Question ◽  
Observation Error ◽  
Moist Processes

Abstract This paper first reviews the current status, issues, and limitations of the parameterizations of atmospheric large-scale and convective moist processes that are used in numerical weather prediction and climate general circulation models. Both large-scale (resolved) and convective (subgrid scale) moist processes are dealt with. Then, the general question of the inclusion of diabatic processes in variational data assimilation systems is addressed. The focus is put on linearity and resolution issues, the specification of model and observation error statistics, the formulation of the control vector, and the problems specific to the assimilation of observations directly affected by clouds and precipitation.

scholarly journals Evaluation of Combined Satellite and Radar Data Assimilation with POD-4DEnVar Method on Rainfall Forecast

2020 ◽  
Vol 10 (16) ◽  
pp. 5493 ◽  
Author(s):  
Jingnan Wang ◽  
Lifeng Zhang ◽  
Jiping Guan ◽  
Mingyang Zhang
Keyword(s):  
Data Assimilation ◽  
Weather Prediction ◽  
Weather Forecast ◽  
Radar Data ◽  
Precipitation Forecast ◽  
Rainfall Forecast ◽  
Skill Scores ◽  
The Individual ◽  
The Impact ◽  
Radar Data Assimilation

Satellite and radar observations represent two fundamentally different remote sensing observation types, providing independent information for numerical weather prediction (NWP). Because the individual impact on improving forecast has previously been examined, combining these two resources of data potentially enhances the performance of weather forecast. In this study, satellite radiance, radar radial velocity and reflectivity are simultaneously assimilated with the Proper Orthogonal Decomposition (POD)-based ensemble four-dimensional variational (4DVar) assimilation method (referred to as POD-4DEnVar). The impact is evaluated on continuous severe rainfall processes occurred from June to July in 2016 and 2017. Results show that combined assimilation of satellite and radar data with POD-4DEnVar has the potential to improve weather forecast. Averaged over 22 forecasts, RMSEs indicate that though the forecast results are sensitive to different variables, generally the improvement is found in different pressure levels with assimilation. The precipitation skill scores are generally increased when assimilation is carried out. A case study is also examined to figure out the contributions to forecast improvement. Better intensity and distribution of precipitation forecast is found in the accumulated rainfall evolution with POD-4DEnVar assimilation. These improvements are attributed to the local changes in moisture, temperature and wind field. In addition, with radar data assimilation, the initial rainwater and cloud water conditions are changed directly. Both experiments can simulate the strong hydrometeor in the precipitation area, but assimilation spins up faster, strengthening the initial intensity of the heavy rainfall. Generally, the combined assimilation of satellite and radar data results in better rainfall forecast than without data assimilation.

Assimilation of Mode-S EHS Aircraft Observations in COSMO-KENDA

2016 ◽  
Vol 144 (5) ◽  
pp. 1697-1711 ◽  
Author(s):  
Heiner Lange ◽  
Tijana Janjić
Keyword(s):  
Data Assimilation ◽  
Forecast Error ◽  
Current Data ◽  
Small Scale ◽  
Observation Error ◽  
Observation Systems ◽  
Aircraft Observations ◽  
Error Standard Deviation ◽  
Flight Level ◽  
Random Thinning

Aircraft observations of wind and temperature collected by airport surveillance radars [Mode-S Enhanced Surveillance (Mode-S EHS)] were assimilated in the Consortium for Small-Scale Modeling Kilometre-scale Ensemble Data Assimilation (COSMO-KENDA), which couples an ensemble Kalman filter to a 40-member ensemble of the convection permitting COSMO-DE model. The number of observing aircrafts in Mode-S EHS was about 15 times larger than in the AMDAR system. In the comparison of both aircraft observation systems, a similar observation error standard deviation was diagnosed for wind. For temperature, a larger error was diagnosed for Mode-S EHS. With the high density of Mode-S EHS observations, a reduction of temperature and wind error in forecasts of 1 and 3 hours was found mainly in the flight level and less near the surface. The amount of Mode-S EHS data was reduced by random thinning to test the effect of a varying observation density. With the current data assimilation setup, a saturation of the forecast error reduction was apparent when more than 50% of the Mode-S EHS data were assimilated. Forecast kinetic energy spectra indicated that the reduction in error is related to analysis updates on all scales resolved by COSMO-DE.

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