Towards high-towards high-resolution dust reanalysis for Northern Africa, the Middle East and Europe

2020 ◽  
Author(s):  
Enza Di Tomaso ◽  
Sara Basart ◽  
Jeronimo Escribano ◽  
Paul Ginoux ◽  
Oriol Jorba ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Middle East ◽  
Data Assimilation ◽  
Dust Storms ◽  
Northern Africa ◽  
Combine Model ◽  
Climate Services ◽  
Deep Blue ◽  
Satellite Retrievals ◽  
The Impact

<p>DustClim (Dust Storms Assessment for the development of user-oriented Climate Services in Northern Africa, Middle East and Europe) is a project of the European Research Area For Climate Services (ERA4CS). DustClim is aiming to provide reliable information on sand and dust storms for developing dust-related services for selected socio-economic sectors: air quality, aviation and solar energy.</p><p>This contribution will describe the work done within the DustClim project towards the production of a dust reanalysis over the domain of Northern Africa, the Middle East and Europe at an unprecedented high spatial resolution (at 10km x 10km) using the state-of-art Multiscale Online Nonhydrostatic Atmosphere Chemistry model (MONARCH) and its data assimilation capability (Di Tomaso et al., 2017). An ensemble-based Kalman filter (namely the local ensemble transform Kalman filter – LETKF) has been utilized to optimally combine model simulations and satellite retrievals.</p><p>Dust ensemble forecasts are used to estimate flow-dependent forecast uncertainty, which is used by the data assimilation scheme to optimally combine model prior information with satellite retrievals. Satellite observations from MODIS Deep Blue with specific observational constraint for dust (Ginoux et al., 2012; Pu and Ginoux, 2016; Sayer et al., 2014) are considered for assimilation over land surfaces, including source regions. MONARCH ensemble has been generated by applying multi-parameters, multi-physics, multi-meteorological initial and boundary conditions perturbations. Sensitive parameters of the assimilation configuration like the balance between observational and background uncertainty, or the spatial location of errors have been carefully calibrated.</p><p>The dust reanalysis for the period 2011-2016 is being compared against independent dust-filtered observations from AERONET (AErosol RObotic NETwork) show the benefit of the assimilation of dust-related MODIS Deep Blue products over areas not easily covered by other observational datasets. Particularly relevant is the improvement of the model skills over the Sahara.</p><p>References:<br>Di Tomaso, E., Schutgens, N. A. J., Jorba, O., and Pérez García-Pando, C. (2017): Assimilation of MODIS Dark Target and Deep Blue observations in the dust aerosol component of NMMB-MONARCH version 1.0, Geosci. Model Dev., 10, 1107-1129, doi:10.5194/gmd-10-1107-2017.<br>Ginoux, P., Prospero, J. M., Gill, T. E., Hsu, N. C. and Zhao, M. Global-Scale Attribution of Anthropogenic and Natural Dust Sources and Their Emission Rates Based on Modis Deep Blue Aerosol Products. Rev Geophys 50, doi:10.1029/2012rg000388 (2012).<br>Pu, B., and Ginoux, P. (2016). The impact of the Pacific Decadal Oscillation on springtime dust activity in Syria. Atmospheric Chemistry and Physics, 16(21), 13431-13448.<br>Sayer, A. M., Munchak, L. A., Hsu, N. C., Levy, R. C., Bettenhausen, C., and Jeong, M.-J.: MODIS Collection 6 aerosol products: Comparison between Aqua’s e-Deep Blue, Dark Target, and “merged” data sets, and usage recommendations, J. Geophys. Res.-Atmos., 119, 13965–13989, doi:10.1002/2014JD022453, 2014.</p><p>Acknowledgement<br>DustClim project is part of ERA4CS, an ERA-NET initiated by JPI Climate, and funded by FORMAS (SE), DLR (DE), BMWFW (AT), IFD (DK), MINECO (ES), ANR (FR) with co-funding by the European Union (Grant 690462). We acknowledge PRACE for awarding access to HPC resources through the eDUST and eFRAGMENT1 projects.</p><p> </p>

A 10-year regional reanalysis of desert dust aerosol at high spatial resolution

2021 ◽  
Author(s):  
Enza Di Tomaso ◽  
Jerónimo Escribano ◽  
Paul Ginoux ◽  
Sara Basart ◽  
Francesca Macchia ◽  
...  
Keyword(s):  
Middle East ◽  
Data Assimilation ◽  
Spatial Resolution ◽  
Weather Prediction ◽  
Reanalysis Data ◽  
Northern Africa ◽  
Dust Aerosol ◽  
Data Set ◽  
Desert Dust ◽  
Deep Blue

<p>Desert dust is the most abundant aerosol by mass residing in the atmosphere. It plays a key role in the Earth’s system by influencing the radiation balance, by affecting cloud formation and cloud chemistry, and by acting as a fertilizer for the growth of phytoplankton and for soil through its deposition over the ocean and land.</p><p>Due to the nature of its emission and transport, atmospheric dust concentrations are highly variable in space and time and, therefore, require a continuous monitoring by measurements. Dust observations are best exploited by being combined with model simulations for the production of analyses and reanalyses, i.e., complete and consistent four dimensional reconstructions of the atmosphere. Existing aerosol (and dust) reanalyses for the global domain have been produced by total aerosol constraint and at relatively coarse spatial resolution, while regional reanalyses exclude some of the regions containing the major sources of desert dust in Northern Africa and the Middle East.</p><p>We present here a 10-year reanalysis data set of desert dust at a horizontal resolution of 0.1°, and which covers the domain of Northern Africa, the Middle East and Europe. The reanalysis has been produced by assimilating in the MONARCH chemical weather prediction system (Di Tomaso et al., 2017) satellite retrievals over dust source regions with specific dust observational constraint (Ginoux et al., 2012; Pu and Ginoux, 2016).</p><p>Furthermore, we describe its evaluation in terms of data assimilation diagnostics and comparison against independent observations. Statistics of analysis departures from assimilated observations prove the consistency of the data assimilation system showing that the analysis is closer to the observations than the first-guess. Temporal mean of analysis increments show that the assimilation led to an overall reduction of dust with pattern of systematic corrections that vary with the seasons, and can be linked primarily to misrepresentation of source strength.</p><p>Independent evaluation of the analysis with AERONET observations indicates that the reanalysis data set is highly accurate, and provides therefore a reliable historical record of atmospheric desert dust concentrations in a recent decade.</p><p><strong>References</strong></p><p>Di Tomaso, E., Schutgens, N. A. J., Jorba, O., and Pérez García-Pando, C. (2017): Assimilation of MODIS Dark Target and Deep Blue observations in the dust aerosol component of NMMB-MONARCH version 1.0, Geosci. Model Dev., 10, 1107-1129.</p><p>Ginoux, P., Prospero, J. M., Gill, T. E., Hsu, N. C. and Zhao, M. (2012): Global-Scale Attribution of Anthropogenic and Natural Dust Sources and Their Emission Rates Based on Modis Deep Blue Aerosol Products. Rev Geophys 50.</p><p>Pu, B., and Ginoux, P. (2016). The impact of the Pacific Decadal Oscillation on springtime dust activity in Syria. Atmospheric Chemistry and Physics, 16(21), 13431-13448.</p><p><strong>Acknowledgements </strong></p><p>The authors acknowledge the DustClim project which is part of ERA4CS, an ERA-NET initiated by JPI Climate, and funded by FORMAS (SE), DLR (DE), BMWFW (AT), IFD (DK), MINECO (ES), ANR (FR) with co-funding by the European Union (435690462); PRACE (eDUST/eFRAGMENT1/eFRAGMENT2), RES (AECT-2020-3-0013/AECT-2019-3-0001/AECT-2020-1-0007) for awarding access to MareNostrum at BSC and for technical support.</p>

scholarly journals An Ensemble Kalman Filter for severe dust storm data assimilation over China

2008 ◽  
Vol 8 (11) ◽  
pp. 2975-2983 ◽  
Author(s):  
C. Lin ◽  
Z. Wang ◽  
J. Zhu
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Dust Storm ◽  
Ensemble Kalman Filter ◽  
Deposition Velocity ◽  
Dust Storms ◽  
Initial Time ◽  
Model Errors ◽  
The Impact ◽  
Severe Dust Storm

Abstract. An Ensemble Kalman Filter (EnKF) data assimilation system was developed for a regional dust transport model. This paper applied the EnKF method to investigate modeling of severe dust storm episodes occurring in March 2002 over China based on surface observations of dust concentrations to explore the impact of the EnKF data assimilation systems on forecast improvement. A series of sensitivity experiments using our system demonstrates the ability of the advanced EnKF assimilation method using surface observed PM10 in North China to correct initial conditions, which leads to improved forecasts of dust storms. However, large errors in the forecast may arise from model errors (uncertainties in meteorological fields, dust emissions, dry deposition velocity, etc.). This result illustrates that the EnKF requires identification and correction model errors during the assimilation procedure in order to significantly improve forecasts. Results also show that the EnKF should use a large inflation parameter to obtain better model performance and forecast potential. Furthermore, the ensemble perturbations generated at the initial time should include enough ensemble spreads to represent the background error after several assimilation cycles.

scholarly journals The impact of Pacific Decadal Oscillation on springtime dust activity in Syria

2016 ◽  
Author(s):  
Bing Pu ◽  
Paul Ginoux
Keyword(s):  
Middle East ◽  
Optical Depth ◽  
North Africa ◽  
Pacific Decadal Oscillation ◽  
Large Scale ◽  
Arabian Peninsula ◽  
Dust Storms ◽  
Near Surface ◽  
Deep Blue ◽  
The Impact

Abstract. The increasing trend of aerosol optical depth in the Middle East and a recent severe dust storm in Syria have raised questions as whether dust storms will increase and promoted investigations on the dust activities driven by the natural climate variability underlying the ongoing human perturbations such as the Syrian civil war. This study examined the influences of the Pacific decadal oscillation (PDO) on dust activities in Syria using an innovative dust optical depth (DOD) dataset derived from Moderate Resolution Imaging Spectroradiometer (MODIS) Deep Blue aerosol products. A significantly negative correlation is found between the Syrian DOD and the PDO in spring from 2003–2015. High DOD in spring is associated with lower geopotential height over the Middle East, Europe, and North Africa, accompanied by near surface anomalous westerly winds over the Mediterranean basin and southerly winds over the eastern Arabian Peninsula. These large-scale patterns promote the formation of the cyclones over the Middle East to trigger dust storms and also facilitate the transport of dust from North Africa, Iraq, and Saudi Arabian to Syria, where the transported dust dominates the seasonal mean DOD in spring. A negative PDO not only creates circulation anomalies favorable to high DOD in Syria but also suppresses precipitation in dust source regions over the eastern and southern Arabian Peninsula and northeastern Africa. On the daily scale, in addition to the favorable large-scale condition associated with a negative PDO, enhanced atmospheric instability in Syria associated with increased precipitation in Turkey and northern Syria is also critical for the development of strong springtime dust storms in Syria.

scholarly journals Assimilation of MODIS Dark Target and Deep Blue observations in the dust aerosol component of NMMB/BSC-CTM version 1.0

2016 ◽  
Author(s):  
Enza Di Tomaso ◽  
Nick A. J. Schutgens ◽  
Oriol Jorba ◽  
Carlos Pérez García-Pando
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Mineral Dust ◽  
Positive Impact ◽  
Chemical Transport ◽  
Naval Research ◽  
Atmospheric Dust ◽  
Deep Blue ◽  
Satellite Retrievals ◽  
Sahel Region

Abstract. A data assimilation system has been developed for the chemical transport forecast model NMMB/BSC-CTM, with a focus on mineral dust, a prominent type of aerosol. Before this work, the system did not have an aerosol data assimilation capability and dust was produced uniquely from model estimated surface emission fluxes. As emissions are recognized as a major factor limiting the accuracy of dust modelling, remote sensing observations from satellites have been used to improve the description of the atmospheric dust load in the model. An ensemble-based Kalman filter technique (namely the Local Ensemble Transform Kalman Filter – LETKF) has been utilized to optimally combine model background and satellite retrievals. Our implementation of the ensemble is based on known uncertainties in the physical parametrizations of the dust emission scheme. We have considered for assimilation satellite Aerosol Optical Depth (AOD) at 550 nm retrieved from measurements of top-of-atmosphere reflectances by the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor on-board the NASA Aqua and Terra satellites, after applying a mineral dust filter. In particular we have assimilated two MODIS Level 3 AOD products: the U.S. Naval Research Laboratory (NRL) and University of North Dakota AOD, which is available over land and ocean, with the exclusion of bright reflective surfaces and is based on the MODIS Dark Target Collection 5 Level 2 product, and the MODIS Deep Blue Collection 6 AOD, which is available over land including bright arid surfaces, such as deserts. Data assimilation experiments using the LETKF scheme have been evaluated against observations from the Aerosol Robotic Network (AERONET) of ground-based stations and against MODIS satellite retrievals. Experiments showed that AOD retrievals using the Dark Target algorithm can help NMMB/BSC-CTM to better characterize atmospheric dust. This is particularly true for the analysis of the dust outflow in the Sahel region and over the African Atlantic coast. The additional assimilation of retrievals based on the Deep Blue algorithm has a further positive impact in the analysis downwind from the strongest dust sources of Sahara and in the Arabian peninsula. An analysis-initialized forecast performs better (lower forecast error and higher correlation with observations) than a standard forecast, with the exception of underestimating dust in the long-range Atlantic transport and degradation of the temporal evolution of dust in some regions after day 1. Particularly relevant is the improved forecast over Sahara throughout the forecast range thanks to the assimilation of Deep Blue retrievals over areas not easily covered by other observational datasets. The present study on mineral dust is a first step towards data assimilation with a complete aerosol chemical transport model that includes multiple aerosol species.

scholarly journals Assimilation of MODIS Dark Target and Deep Blue observations in the dust aerosol component of NMMB-MONARCH version 1.0

2017 ◽  
Vol 10 (3) ◽  
pp. 1107-1129 ◽  
Author(s):  
Enza Di Tomaso ◽  
Nick A. J. Schutgens ◽  
Oriol Jorba ◽  
Carlos Pérez García-Pando
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Mineral Dust ◽  
Positive Impact ◽  
Forecast Error ◽  
Weather Prediction ◽  
Prediction System ◽  
Deep Blue ◽  
Modis Aod ◽  
Sahel Region

Abstract. A data assimilation capability has been built for the NMMB-MONARCH chemical weather prediction system, with a focus on mineral dust, a prominent type of aerosol. An ensemble-based Kalman filter technique (namely the local ensemble transform Kalman filter – LETKF) has been utilized to optimally combine model background and satellite retrievals. Our implementation of the ensemble is based on known uncertainties in the physical parametrizations of the dust emission scheme. Experiments showed that MODIS AOD retrievals using the Dark Target algorithm can help NMMB-MONARCH to better characterize atmospheric dust. This is particularly true for the analysis of the dust outflow in the Sahel region and over the African Atlantic coast. The assimilation of MODIS AOD retrievals based on the Deep Blue algorithm has a further positive impact in the analysis downwind from the strongest dust sources of the Sahara and in the Arabian Peninsula. An analysis-initialized forecast performs better (lower forecast error and higher correlation with observations) than a standard forecast, with the exception of underestimating dust in the long-range Atlantic transport and degradation of the temporal evolution of dust in some regions after day 1. Particularly relevant is the improved forecast over the Sahara throughout the forecast range thanks to the assimilation of Deep Blue retrievals over areas not easily covered by other observational datasets. The present study on mineral dust is a first step towards data assimilation with a complete aerosol prediction system that includes multiple aerosol species.

scholarly journals The impact of MISR-derived injection height initialization on wildfire and volcanic plume dispersion in the HYSPLIT model

2018 ◽  
Vol 11 (11) ◽  
pp. 6289-6307 ◽  
Author(s):  
Charles J. Vernon ◽  
Ryan Bolt ◽  
Timothy Canty ◽  
Ralph A. Kahn
Keyword(s):  
Volcanic Eruptions ◽  
Dust Storms ◽  
Volcanic Plume ◽  
Plume Dispersion ◽  
Hysplit Model ◽  
Particle Injection ◽  
Nominal Model ◽  
Satellite Retrievals ◽  
Aerosol Sources ◽  
The Impact

Abstract. The dispersion of particles from wildfires, volcanic eruptions, dust storms, and other aerosol sources can affect many environmental factors downwind, including air quality. Aerosol injection height is one source attribute that mediates downwind dispersion, as wind speed and direction can vary dramatically with elevation. Using plume heights derived from space-based, multi-angle imaging, we examine the impact of initializing plumes in the NOAA Air Resources Laboratory's Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model with satellite-measured vs. nominal (model-calculated or VAAC-reported) injection height on the simulated dispersion of six large aerosol plumes. When there are significant differences in nominal vs. satellite-derived particle injection heights, especially if both heights are in the free troposphere or if one injection height is within the planetary boundary layer (PBL) and the other is above the PBL, differences in simulation results can arise. In the cases studied with significant nominal vs. satellite-derived injection height differences, the HYSPLIT model can represent plume evolution better, relative to independent satellite observations, if the injection height in the model is constrained by hyper-stereo satellite retrievals.

Evaluation of NMMB-MONARCH dust reanalysis within the DustClim ERA4CS project

2020 ◽  
Author(s):  
Michail Mytilinaios ◽  
Lucia Mona ◽  
Francesca Barnaba ◽  
Sergio Ciamprone ◽  
Serena Trippetta ◽  
...  
Keyword(s):  
Middle East ◽  
Size Distribution ◽  
Optical Depth ◽  
Extinction Coefficient ◽  
Dust Storms ◽  
Northern Africa ◽  
Dust Deposition ◽  
Dust Extinction ◽  
Dust Size Distribution

<p>An advanced dust reanalysis with high spatial (at 10km x 10km) and temporal resolution is produced in the framework of DustClim project (Dust Storms Assessment for the development of user-oriented Climate Services in Northern Africa, Middle East and Europe) [1], aiming to provide reliable information on dust storms current conditions and predictions, focusing on the dust impacts on various socio-economic sectors.</p><p>This regional reanalysis is based on the assimilation of dust-related satellite observations from MODIS instrument [2], in the Multiscale Online Nonhydrostatic Atmosphere Chemistry model (NMMB-MONARCH) [3], over the region of Northern Africa, Middle East and Europe. The reanalysis is now available for a seven-year period (2011-2016) providing the following dust products: Columnar and surface concentration, distributed in 8 dust particle size bins, with effective radius ranging from 0,15μm to 7,1μm, dust load, dry and wet dust deposition, dust optical depth (DOD) and coarse dust optical depth (radius>1μm) at 550nm and profiles of dust extinction coefficient at 550nm.</p><p>A thorough evaluation of the reanalysis is in progress to assess the quality and uncertainty of the dust simulations, using dust-filtered products, retrieved from different measurement techniques, both from in-situ and remote sensing observations. The datasets considered for the DustClim reanalysis evaluation, provide observations of variables that are included in the model simulations. The DOD is provided by AERONET network [4] and by IASI [5], POLDER [6], MISR [7] and MODIS space-borne sensors; Dust extinction profiles are provided by ACTRIS/EARLINET network [8] and CALIPSO/LIVAS dataset [9]; Dust PM10 surface concentrations derived from INDAAF/SDT [10] network and estimated from PM10 measurements [11] performed within EEA/EIONET [12] network; Dust deposition measurements collected by the INDAAF/SDT and the CARAGA/DEMO [13] networks; Dust size distribution from in situ observations (ground-based and airborne); And column-averaged dust size distribution at selected stations from the AERONET network.</p><p>In this work, we present the results of the model evaluation for the year 2012. The first evaluation results will focus on dust extinction coefficient profiles from EARLINET and LIVAS, on DOD using AERONET, MISR and MODIS datasets, and on dust PM10 concentration from INDAAF/SDT network. Moreover, a DOD climatology covering the whole reanalysis period (2011-2016) will be compared with the results obtained from AERONET network.</p><p> </p><p>References</p><p>[1] https://sds-was.aemet.es/projects-research/dustclim</p><p>[2] https://modis.gsfc.nasa.gov/</p><p>[3] Di Tomaso et al., <em>Geosci. Model Dev.</em>, <strong>10</strong>, 1107-1129, doi:10.5194/gmd-10-1107-2017., 2017.</p><p>[4] https://aeronet.gsfc.nasa.gov/</p><p>[5] Cuesta et al., <em>J. Geophys. Res.</em>, <strong>120</strong>, 7099-7127, 2015.</p><p>[6] http://www.icare.univ-lille1.fr/parasol/overview/</p><p>[7] https://misr.jpl.nasa.gov/</p><p>[8] https://www.earlinet.org/</p><p>[9] Marinou et al., <em>Atmos. Chem. Phys.</em>, <strong>17</strong>, 5893–5919, https://doi.org/10.5194/acp-17-5893-2017, 2017.</p><p>[10] https://indaaf.obs-mip.fr/</p><p>[11] Barnaba et al., <em>Atmospheric environment</em>, <strong>161</strong>, 288-305, 2017.</p><p>[12] https://www.eionet.europa.eu/</p><p>[13] Laurent et al., <em>Atmos. Meas. Tech.</em>, <strong>8</strong>, 2801–2811, 2015.</p><p> </p><p> </p><p>Acknowledgement</p><p>DustClim project is part of ERA4CS, an ERA-NET initiated by JPI Climate, and funded by FORMAS (SE), DLR (DE), BMWFW (AT), IFD (DK), MINECO (ES), ANR (FR) with co-funding by the European Union (Grant 690462).</p>

scholarly journals The impact of data assimilation on the prediction of Asian desert dust using an operational 4D-Var system

2018 ◽  
Author(s):  
Angela Benedetti ◽  
Francesca Di Giuseppe ◽  
Luke Jones ◽  
Vincent-Henri Peuch ◽  
Samuel Rémy ◽  
...  
Keyword(s):  
Air Quality ◽  
Data Assimilation ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Asian Dust ◽  
Added Value ◽  
Desert Dust ◽  
Deep Blue ◽  
Dust Events ◽  
The Impact

Abstract. Asian Dust is a seasonal meteorological phenomenon which affects East Asia, and has severe consequences on the air quality of China, North and South Korea and Japan. Despite the continental extent, the prediction of severe episodes and the anticipation of their consequences is challenging. Three one-year experiments were run to assess the skill of the model of the European Centre for Medium-Range Weather Forecasts (ECMWF) in monitoring Asian dust and understand its relative contribution to air quality over China. Data used were the MODIS Dark Target and the Deep Blue Aerosol Optical Depth. In particular the experiments aimed at understanding the added value of data assimilation runs over a model run without any aerosol data. The year 2013 was chosen as representative for the availability of independent Aerosol Optical Depth (AOD) data from two established ground-based networks (AERONET and CARSNET), which could be used to evaluate experiments. Particulate Matter (PM) data from the China Environmental Protection Agency (CEPA) were also used in the evaluation. Results show that the assimilation of satellite AOD data is beneficial to predict the extent and magnitude of desert-dust events and to improve the forecast of such events. The availability of observations from the MODIS Deep Blue algorithm over bright surfaces is an asset, allowing for a better localization of the sources and definition of the dust events. In general both experiments constrained by data assimilation perform better that the unconstrained experiment, generally showing smaller mean normalized bias and fractional gross error with respect to the independent verification datasets. The impact of the assimilated satellite observations is larger at analysis time, but lasts well into the forecast. While assimilation is not a substitute for model development and characterization of the emission sources, results indicate that it can play a big role in delivering improved forecasts of Asian Dust.

scholarly journals Singular-Vector-Based Covariance Propagation in a Quasigeostrophic Assimilation System

2005 ◽  
Vol 133 (5) ◽  
pp. 1295-1310 ◽  
Author(s):  
Alexander Beck ◽  
Martin Ehrendorfer
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Variational Data Assimilation ◽  
Dynamic Information ◽  
Dynamic Background ◽  
Background Error ◽  
Singular Vectors ◽  
Reduced Rank ◽  
The Impact ◽  
Static Background

Abstract Variational data assimilation systems require the specification of the covariances of background and observation errors. Although the specification of the background-error covariances has been the subject of intense research, current operational data assimilation systems still rely on essentially static and thus flow-independent background-error covariances. At least theoretically, it is possible to use flow-dependent background-error covariances in four-dimensional variational data assimilation (4DVAR) through exploiting the connection between variational data assimilation and estimation theory. This paper reports on investigations concerning the impact of flow-dependent background-error covariances in an idealized 4DVAR system that, based on quasigeostrophic dynamics, assimilates artificial observations. The main emphasis is placed on quantifying the improvement in analysis quality that is achievable in 4DVAR through the use of flow-dependent background-error covariances. Flow dependence is achieved through dynamical error-covariance evolution based on singular vectors in a reduced-rank approach, referred to as reduced-rank Kalman filter (RRKF). The RRKF yields partly dynamic background-error covariances through blending static and dynamic information, where the dynamic information is obtained from error evolution in a subspace of dimension k (defined here through the singular vectors) that may be small compared to the dimension of the model’s phase space n, which is equal to 1449 in the system investigated here. The results show that the use of flow-dependent background-error covariances based on the RRKF leads to improved analyses compared to a system using static background-error statistics. That latter system uses static background-error covariances that are carefully tuned given the model dynamics and the observational information available. It is also shown that the performance of the RRKF approaches the performance of the extended Kalman filter, as k approaches n. Results therefore support the hypothesis that significant analysis improvement is possible through the use of flow-dependent background-error covariances given that a sufficiently large number (here on the order of n/10) of singular vectors is used.

scholarly journals Influence of Assimilating Surface Observations on Numerical Prediction of Landfalls of Hurricane Katrina (2005) with an Ensemble Kalman Filter

2014 ◽  
Vol 142 (8) ◽  
pp. 2915-2934 ◽  
Author(s):  
Hailing Zhang ◽  
Zhaoxia Pu
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Hurricane Katrina ◽  
Ensemble Kalman Filter ◽  
Surface Wind ◽  
Initial Time ◽  
Filter Method ◽  
Low Level ◽  
The Impact ◽  
Surface Observations

Abstract A series of numerical experiments are conducted to examine the impact of surface observations on the prediction of landfalls of Hurricane Katrina (2005), one of the deadliest disasters in U.S. history. A specific initial time (0000 UTC 25 August 2005), which led to poor prediction of Hurricane Katrina in several previous studies, is selected to begin data assimilation experiments. Quick Scatterometer (QuikSCAT) ocean surface wind vectors and surface mesonet observations are assimilated with the minimum central sea level pressure and conventional observations from NCEP into an Advanced Research version of the Weather Research and Forecasting Model (WRF) using an ensemble Kalman filter method. Impacts of data assimilation on the analyses and forecasts of Katrina’s track, landfalling time and location, intensity, structure, and rainfall are evaluated. It is found that the assimilation of QuikSCAT and mesonet surface observations can improve prediction of the hurricane track and structure through modifying low-level thermal and dynamical fields such as wind, humidity, and temperature and enhancing low-level convergence and vorticity. However, assimilation of single-level surface observations alone does not ensure reasonable intensity forecasts because of the lack of constraint on the mid- to upper troposphere. When surface observations are assimilated with other conventional data, obvious enhancements are found in the forecasts of track and intensity, realistic convection, and surface wind structures. More importantly, surface data assimilation results in significant improvements in quantitative precipitation forecasts (QPFs) during landfalls.

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