MSG Cloud Physical Properties retrieval and visualization
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1 MSG Cloud Physical Properties retrieval and visualization Jan Fokke Meirink Maarten Plieger John van de Vegte
2 The MSG-CPP Project Goal: Provide an operational service for all products that are derived from the Meteosat Second Generation Cloud Physical Properties algorithm and make them user friendly available on the internet Started in July 2010 W-OND as customer & funder with KS-AK co funding Project members from all 3 KNMI sectors: (Research > Development > Operations) Procured hardware is placed in the Richardson computer center Functional and technical maintenance is placed at I-ID and I-ICT Processing of the complete mission is in progress Portal in place with free access to the data 2
3 Acknowledgements Rob Roebeling (EUMETSAT, Darmstadt) Hartwig Deneke (IfT, Leipzig) Seethala Chellappan (Scripps, San Diego) Erwin Wolters (KS-KA) Wouter Greuell (KS-AK) Brent Maddux (KS-AK) Paul de Valk (W-PPI) Ian van der Neut (I-RD) Ernst de Vreede (I-RD) Robert van Versendaal (I-ID) Arthur Knopper (I-ICT) Henk Kalle (I-ICT) Jasper Spit (I-ICT) Joeri Mastop (I-ICT) Hans den Braber (I-ICT) 3
4 4
5 Overview Part 1: Observations (Jan Fokke) Introduction Satellite & Retrieval algorithm Validation Applications Part 2: Visualization (Maarten) The Open Geospatial Consortium Web Map Service (WMS) Network Common Data Form (NetCDF) Netherlands Atmospheric Data Center (NADC) Demonstration 5
6 Clouds in the climate system Cloud properties Surface incoming solar radiation Precipitation K. Trenberth 6
7 SEVIRI MSG: Meteosat present Channels: 3 VIS/NIR 8 IR 1 HRVIS Nominal spatial resolution: 3x3 km 2 Temporal resolution: 15 minutes 7
8 CPP algorithm overview SEVIRI radiances: 0.6; 0.8; 1.6; 3.9; 8.7; 10.8; 12.0 µm DAK radiative transfer model look-up tables cloud mask cloud top height cloud phase, optical thickness, effective radius, liquid/ice water path ancillary data: surface reflectance, total column water vapour, liquid water clouds precipitation occurence and rate cloud droplet number concentration, geometrical thickness surface incoming solar radiation global / direct / diffuse 8
9 Validation Liquid Water Path (LWP) Precipitation 9
10 Validation LWP: Europe ground-based Microwave Radiometer (MWR) Chilbolton Paris CloudNet Roebeling et al., GRL,
11 Validation LWP: West Africa x Niamey Overall: Accuracy: ~15 g m -2 Precision: ~25 g m
12 Validation LWP: marine stratocumulus clouds Seethala Chellappan,
13 Inter-comparison LWP with MODIS Overall: Bias: ~20% Std.dev.: ~20% 13
14 Validation: precipitation (triple collocation) SEVIRI retrievals (1 satellite) E-OBS gridded dataset ( 5000 rain gauges) radar composite ( 194 European radars) 14
15 Validation: precipitation Rain gauges Weather Radar SEVIRI Reasonable spatial correlation SEVIRI with rain gauges (E-OBS) Weather radar spatial inconsistency: different radar types / retrieval algorithms Roebeling et al., submitted to J. Hydromet. 15
16 Applications GCM evaluation Climatologies Process studies (e.g., cloud-aerosol interaction) Nowcasting Data assimilation 16
17 Applications GCM evaluation Climatologies Process studies (e.g., cloud-aerosol interaction) Nowcasting Data assimilation 17
18 Evaluation regional climate model RACMO Top-of-atmosphere Albedo MSG/GERB RACMO RACMO GERB Greuell et al., J.Clim.,
19 Evaluation RACMO Cloud Cover MSG/SEVIRI RACMO RACMO SEVIRI Greuell et al., J.Clim.,
20 Evaluation RACMO GERB Greuell et al., J.Clim.,
21 Applications GCM evaluation Climatologies Process studies (e.g., cloud-aerosol interaction) Nowcasting Data assimilation 21
22 Climatological maps: surface solar radiation SEVIRI kj/cm 2 klimaatatlas Brent Maddux,
23 Climatological maps: precipitation SEVIRI mean rain rate (mm/hr) Klimaatatlas accumulated precip (mm/yr) Brent Maddux,
24 Data access with OGC Web Services 24
25 What are webservices? Many things are webservices! SOAP, REST, WSDL From Wikipedia: A web service is typically a Web API that is accessed via HTTP and executed on a remote system This presentation will be about Webservices from the Open Geospatial Consortium (OGC) (webservices for geographical data) 25
26 Introduction to OGC services OGC Open Geospatial Consortium currently ~450 organizations standards for geospatial content and services GIS data sharing and processing Services used within the ADAGUC project: ADAGUC: Atmospheric Data Access for the Geospatial User Community, was finished in 2009) Web Map Service (WMS) - for images Web Feature Service (WFS) - for vector data (features) Web Coverage Service (WCS) - for raster data (coverages) WMS is primarily for visualization WFS and WCS are for getting the data 26
27 Web Map Service - WMS A WMS is used to generate visualizations of geospatial data in the form of 2D images suitable for transfer over the World Wide Web 2 metre temperature (K) 27
28 Web Map Services: From data to images + NetCDF Data (big, 5.5Gb) PNG images (small, 180Kb) Combined geographically 28
29 Example: Meteosat and radar Meteosat HRVIS: Satellite view projection Resolution: 3520 x 4660 Radar precipitation: Polar stereographic projection Resolution: 700 x
30 Combined in Dutch Rijksdriehoekstelsel projection Dutch Rijksdriehoekstelsel Resolution: 1000 x
31 Server Web Map Service Requests (HTTP GET requests) Client GetCapabilities GetMap GetLegendGraphic GetFeatureInfo 31
32 KNMI WMS Viewer (ADAGUC) WMS GetMap WMS GetCapabilities WMS GetLegendGraphic WMS GetFeatureInfo 32
33 Web Coverage Service - WCS Similar to WMS, but returns data instead of images Returns observations in a regular spaced grid in many projections Generates data in geotiff, ascii, netcdf 3/4, HDF5, etc Web Map Service: images Web Coverage Service: data 33
34 ADAGUC: Server, viewer and data standard Server Web Map Service Web Coverage Service Contouring, shading, vectors Dimensions: time, pressure, PostgreSQL, GDAL, Cairo, GD, Proj C++ Viewer Web Map Services Dimensions ExtJS GUI framework JavaScript (Browser based)
35 Used data format: ADAGUC - NetCDF4-CF NetCDF4 with Climate and Forecast conventions (CF conventions) Standard names, standard units Identify and compare data Self describing data ISO
36 NADC Processing Suite Ingest Process Distribute Netherlands Atmospheric Data Center (NADC) is an in-house data & processing handling system developed for various satellite instrument missions like Sciamachy, OMI, Gome2 to generate KNMI products. NADC is capable of: Handling multiple data sources, combine them in higher order products by user defined and flexible processing rules and archiving strategies and disseminate these to external parties. All these aspects are being performed in the context of: in an as fast as possible manner, via maintenance user interfaces, and a robust implementation on hardware, data and control level. 36
37 MSG-CPP processing chain at KNMI Realtime OGC storage at web server Level 2 NetCDF ADAGUC Archive OGC storage at web server Level 2 NetCDF ADAGUC WEB / OGC segment MESAR (satellite distribution System) 7*8 files 1 file Level 1 HRIT MSG-CPP Algorithm PGE (incl HRIT read) Level 2 HDF ClimSaf HDF to NetCDF converter PGE 1 file 1 file 96 files Daily Agregation PGE 1 file 1 file Level 2 NetCDF ADAGUC Offline Archive Offline Archive System is running on 8 dedicated HP blades in high availability configuration 37
38 MSG-CPP data can be accessed freely by: NetCDF Data Archive WMS WCS OpenDAP FTP 38
39 Martijn van Dijk (RWS-DID) 39 Cloud Optical Thickness
40 DEMO
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