Enza Di Tomaso, Jerónimo Escribano, Nick Schutgens,

Slides:

Advertisements

Similar presentations

Claire McConnell A new Saharan dust source activation frequency map derived from MSG-SEVIRI IR-channels.

Advertisements

OSE meeting GODAE, Toulouse 4-5 June 2009 Interest of assimilating future Sea Surface Salinity measurements.

Quantitative retrievals of NO 2 from GOME Lara Gunn 1, Martyn Chipperfield 1, Richard Siddans 2 and Brian Kerridge 2 School of Earth and Environment Institute.

Atmospheric Reanalyses Update Mike Bosilovich. ReanalysisHoriz.ResDatesVintageStatus NCEP/NCAR R1T present1995ongoing NCEP-DOE R2T present2001ongoing.

Data assimilation of trace gases in a regional chemical transport model: the impact on model forecasts E. Emili 1, O. Pannekoucke 1,2, E. Jaumouillé 2,

Frascati nov 2009 A.-C. Engvall, A. Stohl, N. I. Kristiansen, A. Fahre Vik, K. Tørseth, and others Norwegian Institue for Air Research NILU Dept.

1 Satellite Remote Sensing of Particulate Matter Air Quality ARSET Applied Remote Sensing Education and Training A project of NASA Applied Sciences Pawan.

Dust Detection in MODIS Image Spectral Thresholds based on Zhao et al., 2010 Pawan Gupta NASA Goddard Space Flight Center GEST/University of Maryland Baltimore.

Direct aerosol radiative forcing based on combined A-Train observations – challenges in deriving all-sky estimates Jens Redemann, Y. Shinozuka, M.Kacenelenbogen,

VALIDATION OF SUOMI NPP/VIIRS OPERATIONAL AEROSOL PRODUCTS THROUGH MULTI-SENSOR INTERCOMPARISONS Huang, J. I. Laszlo, S. Kondragunta,

Applications and Limitations of Satellite Data Professor Ming-Dah Chou January 3, 2005 Department of Atmospheric Sciences National Taiwan University.

Developing a High Spatial Resolution Aerosol Optical Depth Product Using MODIS Data to Evaluate Aerosol During Large Wildfire Events STI-5701 Jennifer.

The use of Sentinel satellite data in the MACC-II GMES pre-operational atmosphere service R. Engelen, V.-H. Peuch, and the MACC-II team.

EUMETSAT METEOROLOGICAL SATELLITE CONFERENCE 15/09/2013 – 20/09/2013, VIENNA EUMETSAT METEOROLOGICAL SATELLITE CONFERENCE 15/09/2013 – 20/09/2013, VIENNA.

Federal Department of Home Affairs FDHA Federal Office of Meteorology and Climatology MeteoSwiss High-resolution data assimilation in COSMO: Status and.

The Centre for Australian Weather and Climate Research A partnership between CSIRO and the Bureau of Meteorology Summary/Future Re-anal.

Aerosol Data Assimilation with Lidar Observations and Ensemble Kalman Filter T. Thomas Sekiyama (MRI/JMA, Japan) T. Y. Tanaka(MRI/JMA, Japan) A. Shimizu(NIES,

A Comparison of the Northern American Regional Reanalysis (NARR) to an Ensemble of Analyses Including CFSR Wesley Ebisuzaki 1, Fedor Mesinger 2, Li Zhang.

Collection 6 update: MODIS ‘Deep Blue’ aerosol Andrew M. Sayer, N. Christina Hsu, Corey Bettenhausen, Myeong-Jae Jeong, Jaehwa Lee.

6 February 2014Hyer AMS 2014 JCSDA Preparation for assimilation of aerosol optical depth data from NPP VIIRS in a global aerosol model Edward J. Hyer 1.

Operational assimilation of dust optical depth Bruce Ingleby, Yaswant Pradhan and Malcolm Brooks © Crown copyright 08/2013 Met Office and the Met Office.

Reanalysis: When observations meet models

Slide 1 Wind Lidar working group February 2010 Slide 1 Spaceborne Doppler Wind Lidars - Scientific motivation and impact studies for ADM/Aeolus Erland.

Applications of Satellite Remote Sensing to Estimate Global Ambient Fine Particulate Matter Concentrations Randall Martin, Dalhousie and Harvard-Smithsonian.

Variational Assimilation of MODIS AOD using GSI and WRF/Chem Zhiquan Liu NCAR/NESL/MMM Quanhua (Mark) Liu (JCSDA), Hui-Chuan Lin (NCAR),

Assimilating chemical compound with a regional chemical model Chu-Chun Chang 1, Shu-Chih Yang 1, Mao-Chang Liang 2, ShuWei Hsu 1, Yu-Heng Tseng 3 and Ji-Sung.

Soil moisture generation at ECMWF Gisela Seuffert and Pedro Viterbo European Centre for Medium Range Weather Forecasts ELDAS Interim Data Co-ordination.

Integration of biosphere and atmosphere observations Yingping Wang 1, Gabriel Abramowitz 1, Rachel Law 1, Bernard Pak 1, Cathy Trudinger 1, Ian Enting.

Satellite observations of AOD and fires for Air Quality applications Edward Hyer Naval Research Laboratory AQAST June, Madison, Wisconsin 15 June.

DEVELOPING HIGH RESOLUTION AOD IMAGING COMPATIBLE WITH WEATHER FORECAST MODEL OUTPUTS FOR PM2.5 ESTIMATION Daniel Vidal, Lina Cordero, Dr. Barry Gross.

Optical properties Satellite observation ? T,H 2 O… From dust microphysical properties to dust hyperspectral infrared remote sensing Clémence Pierangelo.

Estimating the radiative impacts of aerosol using GERB and SEVIRI H. Brindley Imperial College.

QA filtering of individual pixels to enable a more accurate validation of aerosol products Maksym Petrenko Presented at MODIS Collection 7 and beyond Retreat.

Desert Aerosol Transport in the Mediterranean Region as Inferred from the TOMS Aerosol Index P. L. Israelevich, Z. Levin, J. H. Joseph, and E. Ganor Department.

Timothy Logan University of North Dakota Department of Atmospheric Science.

Local Predictability of the Performance of an Ensemble Forecast System Liz Satterfield and Istvan Szunyogh Texas A&M University, College Station, TX Third.

Satellite Aerosol Validation Pawan Gupta NASA ARSET- AQ – GEPD & SESARM, Atlanta, GA September 1-3, 2015.

1 N. Christina Hsu, Deputy NPP Project Scientist Recent Update on MODIS C6 Deep Blue Aerosol Products and Beyond N. Christina Hsu, Corey Bettenhausen,

Synergy of MODIS Deep Blue and Operational Aerosol Products with MISR and SeaWiFS N. Christina Hsu and S.-C. Tsay, M. D. King, M.-J. Jeong NASA Goddard.

Rong-Ming Hu and Randall Martin Inspiring Minds. Retrieval of Aerosol Single Scattering Albedo (SSA)  Determined with radiative transfer calculation.

ICDC7, Boulder September 2005 Estimation of atmospheric CO 2 from AIRS infrared satellite radiances in the ECMWF data assimilation system Richard.

Studying impacts of the Saharan Air Layer on hurricane development using WRF-Chem/EnKF Jianyu(Richard) Liang Yongsheng Chen 6th EnKF Workshop York University.

Integrating satellite fire and aerosol data into air quality models: recent improvements and ongoing challenges Edward Hyer Naval Research Laboratory 6.

High Resolution MODIS Aerosols Observations over Cities: Long Term Trends and Air Quality.

SCIAMACHY TOA Reflectance Correction Effects on Aerosol Optical Depth Retrieval W. Di Nicolantonio, A. Cacciari, S. Scarpanti, G. Ballista, E. Morisi,

MACC-II analyses and forecasts of atmospheric composition and European air quality: a synthesis of observations and models Richard Engelen & the MACC-II.

Daily observation of dust aerosols infrared optical depth and altitude from IASI and AIRS and comparison with other satellite instruments Christoforos.

An Observationally-Constrained Global Dust Aerosol Optical Depth (AOD) DAVID A. RIDLEY 1, COLETTE L. HEALD 1, JASPER F. KOK 2, CHUN ZHAO 3 1. CIVIL AND.

Direct aerosol radiative effects based on combined A-Train observations Jens Redemann, Y. Shinozuka, J. Livingston, M. Vaughan, P. Russell, M.Kacenelenbogen,

AEROCOM AODs are systematically smaller than MODIS, with slightly larger/smaller differences in winter/summer. Aerosol optical properties are difficult.

WORKSHOP ON CLIMATE CHANGE AND AIR QUALITY : part I: Intercontinental transport and climatic effects of pollutants OBJECTIVE: Define a near-term (-2003)

The application of ensemble Kalman filter in adaptive observation and information content estimation studies Junjie Liu and Eugenia Kalnay July 13th, 2007.

Modeling the emission, transport, and optical properties of Asian dust storms using coupled CAM/CARMA model Lin Su and Owen B. Toon Laboratory for Atmospheric.

Preliminary Analysis of Relative MODIS Terra-Aqua Calibration Over Solar Village and Railroad Valley Sites Using ASRVN A. Lyapustin, Y. Wang, X. Xiong,

MODIS Atmosphere Products: The Importance of Record Quality and Length in Quantifying Trends and Correlations S. Platnick 1, N. Amarasinghe 1,2, P. Hubanks.

Satellite Remote Sensing of the Air Quality Health Index Randall Martin, Dalhousie and Harvard-Smithsonian Aaron van Donkelaar, Lok Lamsal, Dalhousie University.

Assessment of Upper atmospheric plume models using Calipso Satellites and Environmental Assessment and Forecasting Chowdhury Nazmi, Yonghua Wu, Barry Gross,

Intercomparison and Evaluation of Dust Prediction Models WMO SDS-WAS Regional Center for Northern Africa Middle East and Europe Presents:

Slide 1 Investigations on alternative interpretations of AMVs Kirsti Salonen and Niels Bormann 12 th International Winds Workshop, 19 th June 2014.

Navy Aerosol and Visibility Forecasting: Use of Satellite Aerosol Data

Fourth TEMPO Science Team Meeting

Extinction measurements

Data Assimilation and Carbon Cycle Working Groups

Vicarious calibration by liquid cloud target

GEO-CAPE to TEMPO GEO-CAPE mission defined in 2007 Earth Science Decadal Survey Provide high temporal & spatial resolution observations from geostationary.

Analysis of Sand and Dust Storms (SDS) between the years 2003 and 2016

Using dynamic aerosol optical properties from a chemical transport model (CTM) to retrieve aerosol optical depths from MODIS reflectances over land Fall.

Development of an advanced ensemble-based ocean data assimilation approach for ocean and coupled reanalyses Eric de Boisséson, Hao Zuo, Magdalena Balmaseda.

Hartmut Bösch and Sarah Dance

Assimilating vertical profiles of dust observations with ensemble meteorological initial and boundary conditions Jeronimo Escribano (1), Enza Di Tomaso.

Presentation transcript:

Satellite data assimilation of dust aerosol observations for the MONARCH forecasting system Enza Di Tomaso, Jerónimo Escribano, Nick Schutgens, Oriol Jorba, Carlos Pérez García-Pando, Francesca Macchia 17/05/2019 Living Planet Symposium 2019, Milano, Italy

Constrainting Mineral Dust Simulations with Observations Model simulations and observations are combined to obtain the ‘best’ estimate of current atmospheric conditions (dust analysis)

Motivation Assess the potential benefit of dedicated dust observation products in dust data assimilation

Motivation Assess the potential benefit of dedicated dust observation products in dust data assimilation Operational dust forecast and dust reanalyses are produced in the framework of aerosol data assimilation, where total AOD is used to constrain all the main aerosol species

Experiment design control simulation Dust component of the NMMB-MONARCH chemical weather system (Pérez et al., 2011)

Experiment design control simulation Dust component of the NMMB-MONARCH chemical weather system (Pérez et al., 2011) NMMB-MONARCH ensemble members are obtained taking into account model and IC/BC uncertainty ensemble forecast

Experiment design control simulation Dust component of the NMMB-MONARCH chemical weather system (Pérez et al., 2011) NMMB-MONARCH ensemble members are obtained taking into account model and IC/BC uncertainty ensemble forecast Role of the ensemble (B matrix): spatial spreading of information from observations statistically consistent increments between neighbouring grid points multivariate analysis

Experiment design An ensemble-based DA scheme: LETKF - usage of a flow-dependent background error covariance - performing the analysis locally data assimilation

Experiment design An ensemble-based DA scheme: LETKF - usage of a flow-dependent background error covariance - performing the analysis locally Dedicated dust observations: MODIS Dark Target AOD in dust-dominated conditions MODIS Deep Blue AOD in dust-dominated conditions IASI dust AOD - ACTRIS lidar dust exinction coefficient profiles data assimilation observations

MODIS Dark Target and Deep Blue, Level3

Assimilated observations NRL MODIS Dark Target, L3 C5 - filtered and corrected, - spatially aggregated, - uncertainty estimation (Zhang and Reid, 2006; Hyer et al., 2010; Shi et al., 2011) - AE, AI filter for dust

Assimilated observations NRL MODIS Dark Target, L3 C5 - filtered and corrected, - spatially aggregated, - uncertainty estimation (Zhang and Reid, 2006; Hyer et al., 2010; Shi et al., 2011) - AE, AI filter for dust MODIS Deep Blue, L3 C6 - aggregation of highest quality L2 - uncertainty model for L2 (Sayer et al., 2014) - AE, AI, counts filter - uncertainty model for L3:

Ensemble Spread Reduction Ensemble free run Data assimilation run Data assimilation lowers the values of the coefficient of variation in the regions where observations are present, which indicates a reduction of the ensemble spread due to the assimilated observations .

Validation of the forecast Better description of current and forecast conditions for dust with data assimilation AERONET site of independent sun photometer observations Validation

MODIS Deep Blue, Level 2

Assimilated observations MODIS Deep Blue, L2 C6 - AE, ω filter - highest quality flag (Ginoux et al., 2012) - uncertainty model based on Sayer et al., 2014

Analysis increments Feedback from assimilation increments

DustClim Project (2017-2020) Produce a high resolution dust reanalysis for Northern Africa, Middle East and Europe covering the satellite era of quantitative aerosol information, and develop dust-related services tailored to specific socio-economic sectors Monthly dust analyses for 2012

IASI dust AOD, Level 3

Aerosol_cci and CMUG projects IASI dust AOD - observations available day time and night time - over ocean and over land (desert) - 10 μm: detection of dust aerosol coarse mode (infrared wavelengths and ‘‘V’’ shaped depression of the Brightness Temperature) - pixel level uncertainty MODIS IASI

IASI analyses CTL (det.) Free-run LMD IMARS MAPIR ULB The seasonal value of AOD is lowered by DA over the strong African sources in all the experiments, with the exception of the MAPIR analysis; The LMD analysis stands out for higher AOD values at latitudes above 40 degree north.

Aerosol, Clouds, and Trace gases (ACTRIS) lidar profiles

Evaluating the potential of ACTRIS‐2 profiles for DA - Model simulations from the BSC model (NMMB-MONARCH) - PollyNet lidar extinction profiles processed by TROPOS 19/04/2017 18Z 20/04/2017 18Z 21/04/2017 18Z 22/04/2017 18Z 23/04/2017 18Z MODIS-Aqua 21/04/2017, NASA Dust AOD

Evaluating the potential of ACTRIS‐2 profiles for DA Event observed by 3 lidar sensors located in Finokalia (Crete), Limassol (Cyprus) and Haifa (Israel) part of the PollyNet (http://polly.tropos.de/) system. Data (with uncertainty estimation) processed by TROPOS. 19/04/2017 18Z Dust extinction coefficient 20/04/2017 18Z Finokalia (Crete) 21/04/2017 18Z Limassol (Cyprus) 22/04/2017 18Z Haifa (Israel) 23/04/2017 18Z Dust AOD

Evaluating the potential of ACTRIS‐2 profiles for DA - Model simulations from the BSC model (NMMB-MONARCH) - PollyNet lidar extinction profiles processed by TROPOS Case study: 19-23 April 2017 Extinction profiles for - ensemble forecast - ensemble analysis - observations Finokalia (Crete) Haifa (Israel) Limassol (Cyprus) correction of a model underestimation of the total column extinction correction in most cases the plume height

Constrainting Mineral Dust Simulations with Observations Model simulations and observations are combined to obtain the ‘best’ estimate of current atmospheric conditions (dust analysis)

Constrainting Mineral Dust Simulations with Observations Model simulations and observations are combined to obtain the ‘best’ estimate of current atmospheric conditions (dust analysis) useful to initialise models and improve predictions used to produce reanalysis reanalyis analysis analysis-initialized prediction monitoring forecast

Thanks to the people taking measurements, maintaining sites, making retrievals and observation products enza.ditomaso@bsc.es