scholarly journals A Heuristic Approach for Precipitation Data Assimilation: Characterization Using OSSEs

2019 ◽  
Vol 147 (9) ◽  
pp. 3445-3466 ◽  
Author(s):  
Andrés A. Pérez Hortal ◽  
Isztar Zawadzki ◽  
M. K. Yau
Keyword(s):  
Data Assimilation ◽  
Initial Conditions ◽  
Grid Point ◽  
Wrf Model ◽  
Ensemble Member ◽  
Absolute Difference ◽  
Time Interval ◽  
State Variables ◽  
Vertical Column ◽  
A New Technique

Abstract We introduce a new technique for the assimilation of precipitation observations, the localized ensemble mosaic assimilation (LEMA). The method constructs an analysis by selecting, for each vertical column in the model, the ensemble member with precipitation at the ground that is locally closest to the observed values. The proximity between the modeled and observed precipitation is determined by the mean absolute difference of precipitation intensity, converted to reflectivity and measured over a spatiotemporal window centered at each grid point of the model. The underlying hypothesis of the approach is that the ensemble members that are locally closer to the observed precipitation are more probable to be closer to the “truth” in the state variables than the other members. The initial conditions for the new forecast are obtained by nudging the background states toward the mosaic of the closest ensemble members (analysis) over a 30 min time interval, reducing the impacts of the imbalances at the boundaries between the different selected members. The potential of the method is studied using observing system simulation experiments (OSSEs) employing a small ensemble of 20 members. The ensemble is produced by the WRF Model, run at a horizontal grid spacing of 20 km. The experiments lend support to the validity of the hypothesis and allow the determination of the optimal parameters for the approach. In the context of OSSE, this new data assimilation technique is able to produce forecasts with considerable and long-lived error reductions in the fields of precipitation, temperature, humidity, and wind.

scholarly journals A Heuristic Approach for Precipitation Data Assimilation: Effect of Forecast Errors and Assimilation of NCEP Stage IV Precipitation Analyses

2020 ◽  
Vol 148 (4) ◽  
pp. 1629-1651
Author(s):  
Andrés A. Pérez Hortal ◽  
Isztar Zawadzki ◽  
M. K. Yau
Keyword(s):  
Data Assimilation ◽  
Grid Point ◽  
Stage Iv ◽  
Ensemble Member ◽  
Precipitation Forecast ◽  
Forecast Errors ◽  
State Variables ◽  
Expanded Ensemble ◽  
The Ideal

Abstract Recently, Pérez Hortal et al. introduced a simple data assimilation (DA) technique named localized ensemble mosaic assimilation (LEMA) for the assimilation of radar-derived precipitation observations. The method constructs an analysis by assigning to each model grid point the information from the ensemble member that is locally closest to the precipitation observations. This study explores the effects of the forecasts errors in the performance of the method using a series of observing system simulation experiments (OSSEs) with different magnitudes of forecast errors employing a small ensemble of 20 members. The ideal experiments show that LEMA is able to produce forecasts with considerable and long-lived error reductions in the fields of precipitation, temperature, humidity, and wind. Nonetheless, the quality of the analysis deteriorates with increasing forecast errors beyond the spread of the ensemble. To overcome this limitation, we expand the spread of the ensemble used to construct the analysis mosaic by considering states at different times and states from forecasts initialized at different times (lagged forecasts). The ideal experiments show that the additional information in the expanded ensemble improves the performance of LEMA, producing larger and long-lived improvements in the state variables and in the precipitation forecast quality. Finally, the potential of LEMA is explored in real DA experiments using actual Stage IV precipitation observations. When LEMA uses only the background members, the quality of the precipitation forecast shows small or no improvements. However, the expanded ensemble improves the LEMA’s effectiveness, producing larger and more persistent improvements in precipitation forecasts.

A NLS-4Dvar Assimilation System of Surface PM2.5 with WRF-CMAQ Model : Observing System Simulation Experiments

2020 ◽  
Author(s):  
Shan Zhang ◽  
Xiangjun Tian ◽  
Hongqin Zhang ◽  
Xiao Han ◽  
Meigen Zhang
Keyword(s):  
Data Assimilation ◽  
Atmospheric Chemistry ◽  
Initial Conditions ◽  
System Simulation ◽  
Fine Particulate Matter ◽  
Wrf Model ◽  
Nonlinear Least Squares ◽  
Simulation Experiments ◽  
Improve Model ◽  
Assimilation System

<p>        While complete atmospheric chemical transport models have been developed to understanding the complex interactions of atmospheric chemistry and physics, there are large uncertainties in numerical approaches. Data assimilation is an efficient method to improve model forecast of aerosols with optimized initial conditions. We have developed a new framework for assimilating surface fine particulate matter (PM<sub>2.5</sub>) observations in coupled Weather Research and Forecasting (WRF) model and Community Multiscale Air Quality (CMAQ) model, based on nonlinear least squares four-dimensional variational (NLS-4DVar) data assimilation method. The NLS-4DVar approach, which does not require the tangent and adjoint models, has been extensive used in meteorological and environmental areas due to the low computational complexity. Two parallel experiments were designed in the observing system simulation experiments (OSSEs) to evaluate the effectiveness of this system. Hourly PM2.5 observations over China be assimilated in WRF-CMAQ model with 6-h assimilation window, while the background state without data assimilation is conducted as control experiment. The results show that the assimilation significantly reduced the uncertainties of initial conditions (ICs) for WRF-CMAQ model and leads to better forecast. The newly developed PM<sub>2.5</sub> data assimilation system can improve PM<sub>2.5</sub> prediction effectively and easily. In the future, we expect emission to be optimized together with concentrations, and integrate meteorological assimilation into aerosol assimilation system.</p>

DART/CAM: An Ensemble Data Assimilation System for CESM Atmospheric Models

2012 ◽  
Vol 25 (18) ◽  
pp. 6304-6317 ◽  
Author(s):  
Kevin Raeder ◽  
Jeffrey L. Anderson ◽  
Nancy Collins ◽  
Timothy J. Hoar ◽  
Jennifer E. Kay ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Climate Model ◽  
Initial Conditions ◽  
Model Development ◽  
Arctic Sea Ice ◽  
Large Set ◽  
State Variables ◽  
Ensemble Data Assimilation ◽  
Ensemble Data ◽  
Atmospheric Forcings

Abstract The Community Atmosphere Model (CAM) has been interfaced to the Data Assimilation Research Testbed (DART), a community facility for ensemble data assimilation. This provides a large set of data assimilation tools for climate model research and development. Aspects of the interface to the Community Earth System Model (CESM) software are discussed and a variety of applications are illustrated, ranging from model development to the production of long series of analyses. CAM output is compared directly to real observations from platforms ranging from radiosondes to global positioning system satellites. Such comparisons use the temporally and spatially heterogeneous analysis error estimates available from the ensemble to provide very specific forecast quality evaluations. The ability to start forecasts from analyses, which were generated by CAM on its native grid and have no foreign model bias, contributed to the detection of a code error involving Arctic sea ice and cloud cover. The potential of parameter estimation is discussed. A CAM ensemble reanalysis has been generated for more than 15 yr. Atmospheric forcings from the reanalysis were required as input to generate an ocean ensemble reanalysis that provided initial conditions for decadal prediction experiments. The software enables rapid experimentation with differing sets of observations and state variables, and the comparison of different models against identical real observations, as illustrated by a comparison of forecasts initialized by interpolated ECMWF analyses and by DART/CAM analyses.

scholarly journals On the sources of hydrological prediction uncertainty in the Amazon

2012 ◽  
Vol 16 (9) ◽  
pp. 3127-3137 ◽  
Author(s):  
R. C. D. Paiva ◽  
W. Collischonn ◽  
M. P. Bonnet ◽  
L. G. G. de Gonçalves
Keyword(s):  
Data Assimilation ◽  
River Basin ◽  
Hydrological Model ◽  
Initial Conditions ◽  
State Variables ◽  
Streamflow Prediction ◽  
Amazon River Basin ◽  
Forecast Uncertainty ◽  
Ensemble Streamflow Prediction ◽  
Hydrological Forecast

Abstract. Recent extreme events in the Amazon River basin and the vulnerability of local population motivate the development of hydrological forecast systems using process based models for this region. In this direction, the knowledge of the source of errors in hydrological forecast systems may guide the choice on improving model structure, model forcings or developing data assimilation systems for estimation of initial model states. We evaluate the relative importance of hydrologic initial conditions and model meteorological forcings errors (precipitation) as sources of stream flow forecast uncertainty in the Amazon River basin. We used a hindcast approach that compares Ensemble Streamflow Prediction (ESP) and a reverse Ensemble Streamflow Prediction (reverse-ESP). Simulations were performed using the physically-based and distributed hydrological model MGB-IPH, comprising surface energy and water balance, soil water, river and floodplain hydrodynamics processes. The model was forced using TRMM 3B42 precipitation estimates. Results show that uncertainty on initial conditions plays an important role for discharge predictability, even for large lead times (∼1 to 3 months) on main Amazonian Rivers. Initial conditions of surface waters state variables are the major source of hydrological forecast uncertainty, mainly in rivers with low slope and large floodplains. Initial conditions of groundwater state variables are important, mostly during low flow period and in the southeast part of the Amazon where lithology and the strong rainfall seasonality with a marked dry season may be the explaining factors. Analyses indicate that hydrological forecasts based on a hydrological model forced with historical meteorological data and optimal initial conditions may be feasible. Also, development of data assimilation methods is encouraged for this region.

scholarly journals Hybrid 4DVAR with a Local Ensemble Tangent Linear Model: Application to the Shallow-Water Model

2016 ◽  
Vol 145 (1) ◽  
pp. 97-116 ◽  
Author(s):  
Douglas R. Allen ◽  
Craig H. Bishop ◽  
Sergey Frolov ◽  
Karl W. Hoppel ◽  
David D. Kuhl ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Shallow Water ◽  
Linear Model ◽  
Bottom Topography ◽  
Grid Point ◽  
Model Error ◽  
Water Model ◽  
State Variables ◽  
Shallow Water Model

Abstract An ensemble-based tangent linear model (TLM) is described and tested in data assimilation experiments using a global shallow-water model (SWM). A hybrid variational data assimilation system was developed with a 4D variational (4DVAR) solver that could be run either with a conventional TLM or a local ensemble TLM (LETLM) that propagates analysis corrections using only ensemble statistics. An offline ensemble Kalman filter (EnKF) is used to generate and maintain the ensemble. The LETLM uses data within a local influence volume, similar to the local ensemble transform Kalman filter, to linearly propagate the state variables at the central grid point. After tuning the LETLM with offline 6-h forecasts of analysis corrections, cycling experiments were performed that assimilated randomly located SWM height observations, based on a truth run with forced bottom topography. The performance using the LETLM is similar to that of the conventional TLM, suggesting that a well-constructed LETLM could free 4D variational methods from dependence on conventional TLMs. This is a first demonstration of the LETLM application within a context of a hybrid-4DVAR system applied to a complex two-dimensional fluid dynamics problem. Sensitivity tests are included that examine LETLM dependence on several factors including length of cycling window, size of analysis correction, spread of initial ensemble perturbations, ensemble size, and model error. LETLM errors are shown to increase linearly with correction size in the linear regime, while TLM errors increase quadratically. As nonlinearity (or forecast model error) increases, the two schemes asymptote to the same solution.

scholarly journals Application of the WRF-LETKF Data Assimilation System over Southern South America: Sensitivity to Model Physics

2016 ◽  
Vol 31 (1) ◽  
pp. 217-236 ◽  
Author(s):  
María E. Dillon ◽  
Yanina García Skabar ◽  
Juan Ruiz ◽  
Eugenia Kalnay ◽  
Estela A. Collini ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Initial Conditions ◽  
Meteorological Data ◽  
Weather Prediction ◽  
Wrf Model ◽  
Convective System ◽  
Efficient Manner ◽  
Maryland College ◽  
Data Assimilation System ◽  
Assimilation System

Abstract Improving the initial conditions of short-range numerical weather prediction (NWP) models is one of the main goals of the meteorological community. Development of data assimilation and ensemble forecast systems is essential in any national weather service (NWS). In this sense, the local ensemble transform Kalman filter (LETKF) is a methodology that can satisfy both requirements in an efficient manner. The Weather Research and Forecasting (WRF) Model coupled with the LETKF, developed at the University of Maryland, College Park, have been implemented experimentally at the NWS of Argentina [Servicio Meteorológico Nacional (SMN)], but at a somewhat lower resolution (40 km) than the operational Global Forecast System (GFS) at that time (27 km). The purpose of this work is not to show that the system presented herein is better than the higher-resolution GFS, but that its performance is reasonably comparable, and to provide the basis for a continued improved development of an independent regional data assimilation and forecasting system. The WRF-LETKF system is tested during the spring of 2012, using the prepared or quality controlled data in Binary Universal Form for Representation of Meteorological Data (PREPBUFR) observations from the National Centers for Environmental Prediction (NCEP) and lateral boundary conditions from the GFS. To assess the effect of model error, a single-model LETKF system (LETKF-single) is compared with a multischeme implementation (LETKF-multi), which uses different boundary layer and cumulus convection schemes for the generation of the ensemble of forecasts. The performance of both experiments during the test period shows that the LETKF-multi usually outperforms the LETKF-single, evidencing the advantages of the use of the multischeme approach. Both data assimilation systems are slightly worse than the GFS in terms of the synoptic environment representation, as could be expected given their lower resolution. Results from a case study of a strong convective system suggest that the LETKF-multi improves the location of the most intense area of precipitation with respect to the LETKF-single, although both systems show an underestimation of the total accumulated precipitation. These preliminary results encourage continuing the development of an operational data assimilation system based on WRF-LETKF at the SMN.

For How Long Should What Data Be Assimilated for the Mesoscale Forecasting of Convection and Why? Part I: On the Propagation of Initial Condition Errors and Their Implications for Data Assimilation

2010 ◽  
Vol 138 (1) ◽  
pp. 242-255 ◽  
Author(s):  
Frédéric Fabry ◽  
Juanzhen Sun
Keyword(s):  
Data Assimilation ◽  
Great Plains ◽  
Incomplete Data ◽  
Weather Forecasting ◽  
Time Window ◽  
Initial Conditions ◽  
Time Interval ◽  
Initial Condition ◽  
Convective Storms ◽  
Assimilation Time

Abstract Data assimilation is used among other things to constrain the initial conditions of weather forecasting models by fitting the model fields to observations made over a certain time interval. In particular, it tries to tie incomplete data with model constraints to detect and correct for initial condition errors. This is possible only if initial condition errors leave their signature on the data assimilated and if the model is capable of faithfully reproducing such signatures. Using simulations of the evolution of convective storms in the Great Plains over an active 6-day period, the propagation of initial condition errors to other variables as well as their effect on the accuracy of the forecasts were investigated. Increasing the assimilation time window boosts the ability of assimilation systems to detect a variety of initial condition errors; however, limits to the predictability of convective events impose a maximum assimilation period that is a function of the type of measurements assimilated as well as of the type of errors one tries to correct for. These findings are then used to suggest changes in assimilation approaches to take into account the different predictability times of the model fields constrained by assimilation.

Artificial Neural Network for Data Assimilation by WRF Model in Rio de Janeiro, Brazil

2020 ◽  
Vol 38 (2) ◽  
Author(s):  
Vinícius Albuquerque de Almeida Albuquerque de Almeida ◽  
Gutemberg Borges França ◽  
Haroldo Fraga Campos Velho ◽  
Nelson F. Favilla Ebecken
Keyword(s):  
Neural Network ◽  
Relative Humidity ◽  
Data Assimilation ◽  
Air Temperature ◽  
Rio De Janeiro ◽  
Mean Squared Error ◽  
Grid Point ◽  
Wrf Model ◽  
Particle Collision ◽  
The Neural Network

ABSTRACTThis study investigates the use of neural networks for data assimilation of local data in the WRF model in Rio de Janeiro, Brazil. Surface and upper-air data (air temperature, relative humidity and wind speed and direction) from airport stations and 6-hour forecast from WRF are used as input for the model and the 3D-Var analysis for each grid point is used as target variable. Periods of 168h from 2014 and 2015 are used with 6h and 12h assimilation cycles for surface and upper-air data, respectively. The neural network model was built using the Multi-Particle Collision Algorithm (MPCA) where different topologies are tested until the optimum solution is found. Results show that the neural network is able to emulate the 3D-Var with root mean squared error (standard deviation), respectively, of 0.31 K (0.37 K), 3.10% (4.04%), 0.63 ms-1 (1.05 ms-1), 1.10 ms-1 (1.56 ms-1) for air temperature, relative humidity, u-component of the wind and v-component of the wind. Also, the results show the neural network method is able to run 71 times faster than the conventional method under similar hardware configurations.RESUMOEste estudo investiga o uso de redes neurais para assimilação de dados locais no modelo WRF no Rio de Janeiro. Dados de superfície e do ar superior (temperatura do ar, umidade relativa e velocidade e direção do vento) das estações do aeroporto e previsão de 6 horas do WRF são usados como entrada para o modelo, e a análise 3D-Var para cada ponto da grade é usada como variável destino. Períodos de 168h de 2014 e 2015 são utilizados com ciclos de assimilação de 6h e 12h para dados de superfície e do ar superior, respectivamente. O modelo de rede neural foi construído usando o algoritmo de colisão de partículas múltiplas (MPCA), onde diferentes topologias são testadas até que a solução ideal seja encontrada. Os resultados mostram que a rede neural é capaz de emular o 3D-Var com raiz do erro quadrático médio (desvio padrão) de 0,31 K (0,37 K), 3,10% (4,04%), 0,63 ms -1 (1,05 ms-1), 1,10 ms-1 (1,56 ms-1) para temperatura do ar, umidade relativa, componente u do vento e componente v do vento. Além disso, os resultados mostram que o método de rede neural é capaz de rodar 71 vezes mais rápido que o método convencional em configurações de hardware semelhantes.

scholarly journals Impact of Multivariate Background Error Covariance on the WRF-3DVAR Assimilation for the Yellow Sea Fog Modeling

2020 ◽  
Vol 2020 ◽  
pp. 1-19
Author(s):  
Xiaoyu Gao ◽  
Shanhong Gao
Keyword(s):  
Data Assimilation ◽  
Yellow Sea ◽  
Initial Conditions ◽  
Wrf Model ◽  
Probability Of Detection ◽  
Dominant Negative ◽  
The Yellow Sea ◽  
Background Error ◽  
Moisture Condition ◽  
Sea Fog

Numerical modeling of sea fog is highly sensitive to initial conditions, especially to moisture in the marine atmospheric boundary layer (MABL). Data assimilation plays a vital role in the improvement of initial MABL moisture for sea fog modeling over the Yellow Sea. In this study, the weather research and forecasting (WRF) model and its three-dimensional variational (3DVAR) data assimilation module are employed for sea fog simulations. Two kinds of background error (BE) covariances with different control variables (CV) used in WRF-3DVAR, that is, CV5 and multivariate BE (CV6), are compared in detail by explorative case studies and a series of application experiments. Statistical verification metrics including probability of detection (POD) and equitable threat scores (ETS) of forecasted sea fog area are computed and compared for simulations with the implementations of CV5 and CV6 in the WRF-3DVAR system. The following is found: (1) there exists a dominant negative correlation between temperature and moisture in CV6 near the sea surface, which makes it possible to improve the initial moisture condition in the MABL by assimilation of observed temperature; (2) in general, the performance of the WRF-3DVAR assimilation with CV6 is distinctly better, and the results of 10 additional sea fog cases clearly suggest that CV6 is more suitable than CV5 for sea fog modeling. Compared to those with CV5, the average POD and ETS of forecasted sea fog area using 3DVAR with CV6 can be improved by 27.6% and 21.0%, respectively.

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