MSG MPEF Products focus on GII Simon Elliott Meteorological Operations Division

Transcription

1 MSG MPEF focus on GII Simon Elliott Meteorological Operations Division MSG Application Workshop, March 2010, Alanya, Türkiye Slide: 1

2 1. What is the MPEF? Meteorological Product Extraction Facility Near real time level 2 products from MTP (METEOSAT 7) Near real time level 2 products from MSG (METEOSAT 8 and 9) Re-processing of products using archived data (currently MTP only) Meteorological product extraction functions Data acceptance Product generation Product distribution Meteorological product extraction functions Monitoring and control Product quality monitoring Reporting Configuration control Slide: 2

3 MSG MPEF in the Ground Segment Context S/C Met Images & Calibration Data IMPF MPEF DADF EXGATE EUMETCast EXGATE Met Forecasts & Foreign Satellite Data and In-situ Observations Met RMDCN GTS/DWD Met Archive ECMWF Slide: 3

4 The operational environment Instance 1 OPE Instance 2 M&C OPE-A OPE-A OPE-B M&C OPE-B Server-1 Server-2 Server-3 Server-4 Server-5 Server-6 Primary Secondary Redundant Redundant Primary Secondary Offline Cluster Offline PC s PQM s Slide: 4

5 Data flow model for the current MSG MPEF EXGATE DADF Images Level 1.5 IMPF ECMWF F/C GTS Obs EXGATE Sun/Sat Angles IDAC Forecast FDAC MDAC Data Acceptance Images F/C Obs Images SCE IDMN F/C RTMR SSMI/S CLA Buffer Data Forecast SCE MPE V/Cal CAL HIRS CLA CSR HPI CTH CDS TOZ AMVR Cal Cal SST AER TH ENC XOFL Product Distribution Cal XDAD XMAR XIMP Calibration UMARF IMPF Offline Observations AVER Verified Product Generation Primary Server Sun/Sat Angles IDAC Images IDMN Buffer Data AMVL GII F/C Data Acceptance RTML Product Generation Secondary Server Slide: 5

6 Data flow model for the revised MSG MPEF Images Level 1.5 Images IDAC Images SCE CLA SCE CLA Calibration XDAD XIMP Calibration Data Sun/Sat Angles Images IDMN Buffer Data ENC Forecast CTH GII ECMWF Forecast GTS Observations HIRS Data FDAC MDAC Data Acceptance Forecast Observations RTMR RTML Forecast Forecast Vicarious Calibration HPI CDS CAL SST AER CSR TOZ XMAR SSMI/S Data MPE XOFL Observations AMVR AMVL AVER TH Product Generation Verified Product Distribution PMNC Commanding Monitoring Events Primary Server LMNC M&C Workstation Commanding Monitoring Events PMNC Redundant Server Slide: 6

7 Controlling of the product production MPEF Product Extraction is schedule driven (i.e. commanding is not required) MPEF Product Extraction is controlled by a schedule The schedule consists of Task-Timetables for each Product Extraction Task (e.g. AMV Generation) A Task-Timetable defines Time Window for the individual activities (e.g. activity for every repeat cycle, or every hour) Time Window is defined by: Earliest Start Time Latest Start Time Latest End Time Slide: 7

8 2. Operational products from MSG MPEF The MSG MPEF produces many operational products in near real time. Full information about the products is available through the Product Navigator, which can be accessed here: Information about the products is also available here: st/index.htm?l=en A subset of products are also made as part of the rapid scanning service from METEOSAT 8. These products correspond exactly with those from the full earth scanning, but for the smaller region and shorter scanning period. Slide: 8

9 MSG MPEF operational product list (I) All sky radiance (ASR) Atmospheric motion vectors (AMV) Climate data set (CDS) Cloud analysis (CLA) Cloud analysis image (CLAI) Mean brightness temperatures and radiances from all thermal (e.g. infrared and water vapour) channels. It includes both clear and cloudy sky radiances and brightness temperatures. Wind vectors at different heights derived by tracking the motion of the clouds and other atmospheric constituents (e.g. water vapour patterns). Reduced resolution scene identification information, e.g. temperature calibration data, cloud amount, sun and satellite position angles. Identification of cloud layers with cloud type and coverage, height and temperature. Identification of scenes type for each image segment. This is an image product derived along with CLA. BUFR BUFR BUFR BUFR GRIB UMARF, EUMETCast UMARF, EUMETCast, GTS UMARF UMARF, EUMETCast, GTS UMARF, EUMETCast Cloud mask (CLM) Presence of clouds or clear sky over land or sea. GRIB UMARF, EUMETCast Clear sky reflector map (CRM) Reflectances from the four MSG solar channels. Seven-day average of cloud-free pixels in the 12:00 UTC and surrounding images. GRIB UMARF, EUMETCast Slide: 9

10 MSG MPEF operational product list (II) Clear sky radiance (CSR) Cloud top height (CTH) Divergence (DIV) Active fire detection (FIR) Global instability index (GII) High resolution precipitation index (HPI) Radiance values for image segments determined as cloud free. Height of highest cloud. Based on a subset of the information derived during Scenes Analysis, but also makes use of other external meteorological data. Calculated directly from the field of the Atmospheric Motion Vectors (AMV) from the Meteosat-8 WV6.2 An image-based product that displays information on the presence of fire within a pixel. Atmospheric air mass instability. Produced at EUMETSAT for the full MSG disk every 15 minutes. BUFR GRIB GRIB GRIB, TEXT BUFR UMARF, EUMETCast, GTS UMARF, EUMETCast UMARF, EUMETCast UMARF, EUMETCast Estimates of hourly accumulated precipitation. Internal UMARF UMARF, EUMETCast, GTS Slide: 10

11 MSG MPEF operational product list (III) Multi-sensor precipitation estimate (MPE) Near-real-time rain rates, in pixel resolution, combining polar orbiter microwave measurements with Meteosat IR images. Most suitable for convective precipitation. GRIB UMARF, EUMETCast Tropospheric humidity (TH) Relative humidity in both mid and upper layers of the troposphere. BUFR UMARF, EUMETCast, GTS Total ozone (TOZ) Total density of ozone in atmospheric column for each image segment, based on the SEVIRI 9.7 µm Ozone channel and other IR and WV channels. BUFR UMARF, EUMETCast, GTS Soon to be added Aerosol over sea (AES) Information on the aerosols present in the atmosphere over ocean. GRIB UMARF, EUMETCast Volcanic ash detection (VOL) Information on the presence of volcanic ash within a cloudy pixel. TEXT UMARF, EUMETCast, GTS Normalised differential vegetation index (NDVI) Land surface characteristic important for agricultural, forest, land use and other applications. HDF UMARF, EUMETCast Slide: 11

12 Global Instability Index in more detail Optimal estimation method First guess profile Radiative transfer model Brightness temperatures Compare with sat obsn Refined atmos profile Retrieved atmos profile Radiative transfer model RTTOV 7 (later 9.3) from NWP SAF Repeat until converged Radiative transfer model Instability parameter s more info available here ( model is also used to compute the weighting function matrix which is usually referred to as Jacobians Slide: 12

13 Global Instability Index in more detail Parameters retrieved K Index (KI) KO Index (KO) Lifted Index (LI) Maximum Buoyancy (MB) Precipitable Water Content Index (TPW) Layer Precipitable Water (LPW) content between the TOA and 500 hpa Layer Precipitable Water content between 500 hpa and 850 hpa Layer Precipitable Water content between 850 hpa and the surface The instability indices are defined as K Index KI = (T obs(850) - T obs(500)) + TD obs(850) - (T obs(700) - TD obs(700)) KO Index KO = 0.5 * ( θe obs(500) + θe obs(700) - θe obs(850) - θe obs(1000)) Lifted Index LI = T obs - T lifted from surface at 500 hpa Maximum Buoyancy MB = θe obs(maximum between surface and 850) θe obs(minimum between 700 and 300) where: T obs is the observed temperature TD obs is the observed dew point temperature θe obs is the observed equivalent potential temperature all at the indicated pressure level (in hpa). Slide: 13

14 GII Some Examples K-Index and corresponding IR10.8 image Retrieval is only possible over clear sky! Slide: 14

15 GII- Some Examples K-Index: forecasted by ECMWF and MSG retrieval: Value added by satellite Slide: 15

16 SAWS "Verification" Activities K-Index is related to number of lightning strokes within the next 12 hours. A thus defined POD is typically Slide: 16

17 GII at SAWS: Detailed Case Study 01 Feb 2008 Slide: 17

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20 Our Experience GII indices can be seen as an early warning for possibly severe convection in the 6-12 hour time frame There are however false alarms (e.g. when we have a capping inversion) a trigger mechanism for convection still needs to happen There are much less real misses usually when the GII indicates really stable conditions, convection will not occur. GII can add value over the model, mostly in location of strong gradients and local extremes. Slide: 20

21 3. Product validation The products are intensively validated before they are made operational. They are also continually monitored as part of routine operations. Several mechanisms are use to validate the products: Comparison with in-situ observations received from the GTS. Data such as radiosonde profiles and aircraft reports are routinely decoded, co-located and used for monitoring Long term analysis of statistical data from an offline database gives information about trends and sudden changes in the data. This uses Oracle and a web based front end for reporting Comparison with corresponding products generated with the NWC SAF software can also lead to improvements in the algorithms. The GEO part is already running routinely and the PPS part is currently being introduced. External partners such as ECMWF also provide feedback on the quality of products based on their experience of the data in their centres. Slide: 21

22 4. Product formats All products are encoded into their delivery formats and packed into bulletins and files ready for dissemination inside the MPEF. The formats used depend upon the requirements of the user community receiving the data: Near real time products for NWP are encoded in GRIB 2 and BUFR. These data are disseminated via the GTS/RMDCN and via DVB-S on EUMETCast Near real time products concerning fire and (soon) volcanic ash detection are in text, and being changed to use CAP Forthcoming NDVI products from MSG have been requested in HDF5 Offline data are available from the data centre in a number of formats, with netcdf to be additionally offered Slide: 22