ESA Explorer mission EarthCARE: Earth Clouds, Aerosols and Radiation Explorer Joint ESA/JAXA mission Launch 2016 Budget 700 MEuro

EarthCARE Mission Objectives To quantify cloud-aerosol-radiation interactions so they may be included correctly in weather & climate models  To observe vertical profiles of natural and anthropogenic aerosols, their radiative properties and interaction with clouds  To observe vertical distributions of liquid water and ice, their transport by clouds and their radiative impact  To observe cloud distribution and the characteristics of vertical motions within clouds  To retrieve profiles of radiative heating rate through the combination of the retrieved cloud and aerosol properties EarthCARE measurements will link cloud, aerosols and radiation at a target accuracy of 10 Wm -2

EarthCARE Payload Cloud Profiling Radar (CPR) – 94 GHz – 2.5-m dish – Doppler capability – Min detectable signal -35 dBZ Atmospheric Lidar (ATLID) – 355 nm (UV) with depolarization channel – High spectral resolution capability providing direct cloud/aerosol extinction measurements Multispectral Imager (MSI) – 4 solar and 3 thermal infrared channels – 150-km swath Broadband Radiometer (BBR) – Long-wave & total- wave – 3 views to get fluxes

24/01/2013ECARE overview KNMI

EarthCARE Viewing geometry Satellite mass: 2000 kg Solar panel area: 21 m 2 Altitude: 393 km to maximize sensitivity Radar and lidar power consumption: 2.5 kW

EarthCARE: UK Involvement Lead European Scientist – Professor Anthony Illingworth (University of Reading) Development of synergy algorithms – Professor Robin Hogan (University of Reading) – Sustained funding from NCEO and NERC in support of this activity Development of Doppler radar simulator – Dr Alessandro Battaglia (University of Leicester) Prime contractor – Astrium UK Multi-Spectral Imager – Surrey Satellite Technology Ltd. (Sevenoaks) Broad-Band Radiometer – SEA, Bristol Thermodynamic data for for EarthCARE retrievals; Real-time assimilation of EarthCARE data – European Centre for Medium Range Weather Forecasts (Reading)

ATLID Level 1 Attenuated backscatter in Rayleigh channel Co-polar Mie channel Cross-polar Mie channel ATLID Level 1 Attenuated backscatter in Rayleigh channel Co-polar Mie channel Cross-polar Mie channel CPR Level 1 Radar reflectivity profile, Doppler profile CPR Level 1 Radar reflectivity profile, Doppler profile MSI Level 1 TOA radiances for 4 solar channels, TOA brightness temperatures for 3 thermal channels MSI Level 1 TOA radiances for 4 solar channels, TOA brightness temperatures for 3 thermal channels BBR Level 1 TOA long-wave and total-wave radiances BBR Level 1 TOA long-wave and total-wave radiances ATLID Level 2 Feature mask and target classification, extinction, backscatter and depolarization profiles, aerosol properties, ice cloud properties ATLID Level 2 Feature mask and target classification, extinction, backscatter and depolarization profiles, aerosol properties, ice cloud properties CPR Level 2 Radar echo product, feature mask, cloud type, liquid and ice cloud properties, vertical motion, rain and snow estimates CPR Level 2 Radar echo product, feature mask, cloud type, liquid and ice cloud properties, vertical motion, rain and snow estimates MSI Level 2 Cloud mask, cloud micro- physical parameters, cloud top height, aerosol parameters MSI Level 2 Cloud mask, cloud micro- physical parameters, cloud top height, aerosol parameters BBR Level 2 Unfiltered TOA short-wave and long-wave radiances, TOA short-wave and long- wave fluxes BBR Level 2 Unfiltered TOA short-wave and long-wave radiances, TOA short-wave and long- wave fluxes Synergistic Level 2 Cloud and aerosol products derived from synergistic retrievals using combinations of ATLID, CPR, MSI Synergistic Level 2 Cloud and aerosol products derived from synergistic retrievals using combinations of ATLID, CPR, MSI Radiative Transfer Products Fluxes, heating rates, TOA radiances calculated from constructed 3D cloud-aerosol scenes (1D & 3D rad. transfer) Radiative Transfer Products Fluxes, heating rates, TOA radiances calculated from constructed 3D cloud-aerosol scenes (1D & 3D rad. transfer) Assessment Comparison of Radiative Transfer Products (fluxes, radiances) to BBR radiances and fluxes Assessment Comparison of Radiative Transfer Products (fluxes, radiances) to BBR radiances and fluxes EarthCARE products Lidar Radar Imager BB Radiometer Raw measurements Single instrument products Synergy products Radiation and closure products

The A-Train versus EarthCARE The A-Train (fully launched 2006) – NASA – Multiple platforms – 700-km orbit – CloudSat 94-GHz radar – Calipso 532/1064-nm lidar – CERES broad-band radiometer – MODIS multi-wavelength radiometer EarthCARE (launch 2016) – ESA and JAXA – Single platform – 393-km: higher sensitivity – 94-GHz Doppler radar – 355-nm High spectral res. lidar – 3-view broad-band radiometer – Multi-spectral imager

Example A-train observations Supercooled liquid cloud Radar sees rain but lidar is attenuated Aerosol Lidar more sensitive to ice cloud Air molecules

What would EarthCARE see? Method: Use a variational retrieval algorithm to retrieve microphysical properties of liquid cloud, ice cloud, rain and aerosol Next: Forward model the EarthCARE instruments

Simulated EarthCARE radar EarthCARE radar significantly more sensitive than CloudSat Much more information on thin ice clouds CloudSat radar

Doppler in convective clouds Ice fall speed: important uncertainty in global models − NASA ER2: high flying aircraft Convective updrafts: first time they will be sampled globally

High spectral resolution lidar (HSRL) Lidars detect particles (clouds and aerosol) and air molecules Molecules are fast moving and so have a large Doppler shift EarthCARE’s HSRL can separate the two components using the frequency spectrum of the returned signal EarthCARE will derive extinction profile (crucial for radiative transfer studies) from attenuated air backscatter

Simulated EarthCARE lidar Calipso lidar EarthCARE can separate out particle (Mie) from air (Rayleigh) backscatter Cloud and aerosol extinction profiles can be retrieved directly Attenuated particle backscatter Attenuated air backscatter

Observations Simulated observations from NICAM model Visible image Tropical Cyclone Fengsheng Longwave flux CloudSat 94 GHzCALIPSO 532 nm lidar CloudSat 94 GHz radar echo [dBZ] Brightness temp. (10.8  m) Tropical cyclone: model and observations

Both models lack high cirrus; Met Office has too narrow a distribution of in-cloud IWC Using this work, ECMWF have developed a new scheme that performs better Ice water content and particle size will be considerably more accurate from EarthCARE Delanoe et al. (QJRMS 2011) Evaluation of model ice clouds using A-train retrievals In-cloud mean ice water content Gridbox-mean ice water content

Reading, UK © ECMWF 2013 ECMWF data assimilation activities Marta Janiskova, Sabatino Di Michele et al. In preparation for the launch of EarthCARE, ECMWF are testing the assimilation of CloudSat and Calipso data using a 1D+4D-Var approach (Janikova et al. 2012) Bias correction applied Results show a significant impact on analyses and forecasts EarthCARE data are anticipated to be assimilated by ECMWF into their model when the satellite is launched Case over Pacific

Reading, UK © ECMWF D-Var of cloud radar reflectivity - assimilation Observations First guess Analysis Improved match to observations after assimilation Case over Pacific

Reading, UK © ECMWF 2013 Increments due to 1D-Var assimilation Temperature increments: Specific humidity increments:

Reading, UK © ECMWF 2013 Measures of success: PDF of first-guess minus analysis Cloud radar reflectivity comparison –assimilated observations –PDF has narrowed: indicates assimilation is working Cloud lidar backscatter comparison –independent observations –PDF has narrowed: indicates analysis has been improved

Reading, UK © ECMWF 2013 Case GOES-12 observations 3-hour accumulated NEXRAD precipitation 10  m simulated TB from 9-hour forecast: |obs-exp|- |obs-ref| 3-hour accumulated model precipitation: |obs-exp|- |obs-ref| Impact of 1D+4D-Var assimilation on subsequent forecast

© Crown copyright Met Office EarthCARE and the Met Office Improving the simulation of clouds in our models is important for both short-term forecasting and for reducing uncertainty in our projections of future climate change. EarthCARE will provide information of relevance to both of these aims. We plan to use EarthCARE data to compare with our weather and climate models to see how realistically we represent clouds. This will help us to improving the representation of clouds in our models, including their formation, development and evolution over different time scales.