1 Nowcasting applications Putsay Mária, M Hungarian Meteorological Service Principles of Satellite Meteorology, Online Course, 20 September 2011
2 Outlines Satellite images Derived products SAFNWC/MSG products (continue) plans for the next 5 years SAFNWC/PPS products MPEF products for nowcasting purposes Automatic/interactive applications in Hungary
3 Outlines Satellite images Derived products SAFNWC/MSG products (continue) plans for the next 5 years SAFNWC/PPS products MPEF products for nowcasting purposes Automatic/interactive applications in Hungary
4 Satellite images The main tool for interactive nowcasting applications are the images (single and RGB) Geostationary satellite polar satellites (northern latitudes) There were several presentations about satellite image interpretation, I will speak about derived products for nowcasting applications. Where are you from?
5 Outlines Satellite images Derived products SAFNWC/MSG products (continue) plans for the next 5 years SAFNWC/PPS products MPEF products for nowcasting purposes Automatic/interactive applications in Hungary
6 Nowcasting SAF Satellite Application Facility in support of Nowcasting and Very Short-range Forecasting Objectives: Development of Nowcasting products derived from MSG and Polar satellite systems deliver the SW Packages to users User's support tasks through Help Desk Two software packages to produce products for nowcasting purposes: SAFNWC/MSG, to process geo satellites SAFNWC/PPS, to process polar satellites National Meteorological Institute has to run it locally The products may be used as input to a program e.g. to an objective meso-scale analysis (which in turn may feed a simple nowcasting scheme), as an intermediate product input to other products, as a final image product for display at a forecaster 's desk, or for case studies.
7 Products overview SAFNWC/MSG Clouds Precipitation & Convection Clear Air MSG SW package Cloud Mask (+ dust, volcanic ash) Cloud Type (+ cloud phase) Cloud Top Temperature, Pressure, Height Precipitating Clouds (probability of pr.) Convective Rainfall Rate Rapidly Developing Thunderstorms Total/layer Precipitable Water Stability Analysis Imagery (Statistical and Physical retrieval) Air Mass Analysis Wind Conceptual Models High Resolution Winds Automatic Satellite Image Interpretation All products can be created from 15- and 5-minute data, except ASII.
8 Cloud products Cloud mask (CMa), Cloud Type (CT), Cloud top temp. and height (CTTH) Cloud mask (cloudy, not cloudy, partly cloudy) + dust and volcanic ash cloud detection Cloud type (21 classes of cloudy and cloud free) + cloud phase (ice/water droplets, not class.) Cloud top temperature, pressure, height, effective cloudiness (cloud amount*emissivity) We think these are the most reliable products of the NWC SAF 5- and 15-minute data processing Cloud top phase dust cloud Cloud Mask, Cloud Type frequently serve as a basis for other cloud/surface products.
9 27 April :10 UTC Undefined snow, ice cloud filled cloud cont. cloud free Cloud Mask Cloud Type not proc day&night RGB Cloud Top Pres.
10 Recent improvements Use of HRV to earlier detect the small size clouds/ convection HRV CT with HRV VIS0.6 CT without HRV 27/02/ h45 UTC
11 Cloud Type v :55 UTC Sajat példa utc HRV fog (IR1.6,HRV,HRV) Improvement in v2009 Better detection of the very low clouds/fogs at twilight (-3 o > sun elevation <10 o ) Fog - very important in Central-Europe IR10.8-IR3.9 positive at night, negative daytime Cloud Type v2008 Meteorológiai Tudományos Napok november night microph. RGB
12 Cloud Type v :55 UTC HRV fog (IR1.6,HRV,HRV) Cloud Type v2008 Meteorológiai Tudományos Napok november night microph. RGB
13 Cloud Type v :55 UTC HRV fog (IR1.6,HRV,HRV) Cloud Type v2008 Meteorológiai Tudományos Napok november night microph. RGB
14 Cloud Type v December 2007 Mid-level cloud Improvement of the CT in cold air pool situation (temperature inversion) IR8.7-IR10.8: it is less for low clouds than for mid-level clouds Retrieved cloud top pressure Low-level cloud Measured brightness temperature Cloud Type v2010
15 Snow at night Dust RGB, :55 UTC Snow detection only daytime Key: refl in NIR1.6, IR3.9 At night IR108-IR39 and IR108-IR87 Where is snow?
16 Cloud detection improvement over cold ground at night The cloud mask behavior is very sensitive to the NWP model data. Snow detection only daytime Key: refl in NIR1.6, IR3.9 At night IR108-IR39 and IR108-IR Dust RGB, 02:55 UTC CT v2011, 08:55 UTC Snow detection only daytime CT v2010, 02:55 UTC Cloudy or cloud-free? CT v2011, 02:55 UTC
17 Precipitation products PC: precipitating clouds (probability of precipitation), CRR: Convective rain rate 1 hour accumulated rain 07 June :30/14:25 UTC Useful if no radar data are available: Over ocean Breakdown of radar Area not covered by radar radar IR10.8 CRR useful when and where convection is dominant. CRR PC
18 Do you see some interesting cloud top feature? 07 June :30/14:25 UTC radar IR10.8 CRR PC
19 Comparison of NWCSAF CRR and radar measurements in South Africa 19 December :00 UTC NWC SAF CRR (mm/h) Data from E. de Coning and C. Marcos Radar (Cmax dbz) CRR gives rather strong precipitation, while radar gives only low signal. This area is blocked for radars due to the mountains.
20 NWCSAF HRW High Resolution Wind :10 UTC Red hpa Blue hpa Yellow hpa Green hpa 15-min image sequence cloud motion wind field, including wind pressure level information, calculated from HRV and IR10.8 images
21 HRW product can be useful for: - Nowcasting monitoring tasks: * Flux displacement * Watch and warning of dangerous strong wind situations * Convergence & divergence (specially around cloud systems) * Small scale circulation * Wind singularities. - Assimilation in: * Analysis and Forecasting applications (including NWP models).
22 HRW case study 27th-28th February 2010 Rapid Cyclogenesis Xynthia The strongest satellite retrieved winds (15min mean winds > 89 km/h) in the hpa layer Surface wind observation The diagonal path followed by the strongest winds is similar in both figures. Temporal resolution of HRW is higher than that of the model forecasted winds. HRW can be helpful for watch and warning tasks.
23 The temporal evolution of HRW winds permits also to identify when the hardest winds are striking.
24 Total and Layered Precipitable Water and Instability Indexes NWC SAF Physical Retrieval (SPhR) IR13.4 TPW IR12.0 Spatial interpolation BL (sfc-850) HRIT images IR10.8 ML ( ) WV7.3 PGE13 YYYYMMDDhhmm region config_file H5 HL (500-top) WV6.2 LI PGE01 Cloud Mask Ancillary & coefficients Lon, lat, zenith Bias correction, regression, EOF, etc SHOWALTER K-INDEX + differences (phy. Ret NWP)
25 Mid-level ( hpa) Layer Precipitable Water 23 June 2005 IR10.8 on cloudy areas + LPW on cloud-free areas
26 Time series of IR10.8 : Detection, tracking of cloud systems. Discrimination of convective clouds from non-convective clouds (IR10.8, WV6.2, WV7.3, IR8.7 and IR12.0). It calculates many useful parameters for each tracked cloud system/cell + lightning data (optional) IR RDT apid Developing Thunderstorm :15
27 Rapid Developing Thunderstorm :45 UTC Case study of the developers Small cell showing all signs of rapid developing. Case studies/automatic applications
28 In-build parallax correction tool in the SAFNWC/MSG program package to correct all the 3 km resolution channels + Cloud and PC products Parallax shift Nadir view, or surface observations (e.g. radar) Geostationary view Projection of various cloud-top features to surface ("georeferencing these) strongly depends on their height above ground and on scanning geometry
29 PC with radar Parallax corrected PC with radar Important at verification with radar or with surface measurements. radar (black&whith)
30 Outlines Satellite images Derived products SAFNWC/MSG products (continue) plans for the next 5 years SAFNWC/PPS products MPEF products for nowcasting purposes Automatic/interactive applications in Hungary
31 Next 5 year phase: extending the operability to other satellites Single software for all geostationary satellites Cloud Type GOES-W GOES-E MSG MTSAT GOES-W SAFNWC package scientifically adapted by Météo-France
32 Ash Flag from volcano Puyehue, Chile :30 UTC extracted from MSG and MTSAT :30 UTC the dust RGB (MSG) and ash flag (MTSAT)
33 K-ASII Adaptation of NWC SAF ASII product to the MTSAT-1R Courtesy of Jun-Dong Park, NMSC- KMA (Korea) Caribbean K-ASII Adaptation of NWC SAF RDT product to the GOES-R provided by Yann Guillou, MétéoFrance RDT
34 To develop new products and improve the current ones Cloud top Microphysics Cloud phase Effective radius Condensed water path
35 To prepare for MTG and Post-EPS MTG-LI application to CRR Radar (PPI) Lightning information CRR without lightning CRR with lightning
36 To prepare for MTG and Post-EPS 3D wind from MTG-IRS (possibility to calculate wind shear) MTG-IRS application to Lifted Index
37 Outlines Satellite images Derived products SAFNWC/MSG products (continue) plans for the next 5 years SAFNWC/PPS products MPEF products for nowcasting purposes Automatic/interactive applications in Hungary Do you use data/images of polar satellites? Are you forecaster?
38 NWCSAF/PPS program package to process the data of the polar satellites: NOAA, METOP/AVHRR + a microwave channel for PC Near future: EOS/MODIS and NPP/VIIRS data Cloud mask Cloud top temperature and height Cloud type Precipitating clouds see next slide Near future: additional products: cloud top microphysical parameters: Cloud Optical Thickness, Cloud Phase, Effective Radius (averaged particle size), Liquid/Ice Water path (how much water content below!) NWCSAF webpage real time images + description
39 AVHRR RGB (1,2,4) SAFNWC/PPS Precipitating Clouds (PC) precipitation likelihood from AVHRR + microwave data. No precipitation (rain rate < 0.1 mm/hr) light precipitation: mm/hr Light/moderate precipitation: mm/hr Intensive (convective) precipitation: >5.0 mm/hr 23 May :27 UTC PC RGB (Red: risk of intense, Green: moderate, Blue: light p.) Radar UTC
40 SMHI webpage - real time images - global reception METOP-A Also local reception for some regions all PPS products Cloud Type visible RGB Cloud Top Pressure IR RGB
41 Outlines Satellite images Derived products SAFNWC/MSG products (continue) plans for the next 5 years SAFNWC/PPS products MPEF products for nowcasting purposes Automatic/interactive applications in Hungary
42 MPEF products for nowcasting purposes Products Not software Cloud Mask clear sky over water, clear sky over land, cloud, or not processed. Cloud Analysis cloud type, coverage, height and temperature Cloud Top Height of highest cloud. Volcanic Ash Detection
43 Atmospheric Motion Vectors derived from VIS0.8, WV6.2, WV7.3, IR10.8 and the HRV, by tracking the motion of clouds /water vapor patterns. Each AMV is assigned to a height. (5- and 15-min AMVs) Divergence The Divergence is calculated from the field of the WV6.2 AMVs. High level (above 400 hpa) vectors are considered. Horizontal Divergence and Relative Vorticity on a 32 x 32 pixel grid.
44 Multi-Sensor Precipitation Estimate (5- and 15-minute) rain rates in mm/hr, pixel resolution Combination of polar orbiter microwave measurements and IR10.8 channel data. Most suitable for convective precipitation in areas with poor or no radar coverage, especially in Africa and Asia. Tropospheric Humidity Relative humidity in mid and upper layers of the troposphere. Mean RH of ~ hpa layer derived from WV6.2 Mean RH of ~ hpa layer derived from WV7.3 Res. 16x16 pixel
45 Global Instability Index Total Precipitable Water and instability in cloud free areas. 3x3p. RII 5min, 1x1 pixel IR10.8 TPW :15 UTC IR10.8 Max bouyancy 3:15 UTC IR10.8 K0-index 3:15 UTC IR10.8 K-index 3:15 UTC x Animation IR10.8 IR10.8 Lifted-index 3:15 UTC :20 UTC
46 Outlines Satellite images Derived products SAFNWC/MSG products (continue) plans for the next 5 years SAFNWC/PPS products MPEF products for nowcasting purposes Automatic/interactive applications in Hungary
47 Applications of the NWC SAF/MPEF products at the Hungarian Meteorological Service Interactive applications HAWK software Visualization tool Visualization for the forecasters evaluation of case studies Verification of the satellite products (eg. with radar) of NWP forecasts, simulations with the satellite images/products What is visualized regularly? 4 single channels + RGBs Some of the NWCSAF products (CT, CTTH, RDT, ASII, PC, HRW) MPEF RII (GII) Cloud amount, infra-cloud image Most often used products by the duty forecasters: Winter period looking CT mainly to see the foggy areas (beside RGBs) All year aviation meteorologists use CTTH to see the cloud top height (beside radar cloud top height) (eg. in-cloud icing)
48 Automatic applications Using at assimilation into the ALADIN/HU Numerical Weather Prediction Model Assimilated satellite data/product into the ALADIN/HU - MPEF satellite retrieved wind product (AMV) (plan: SAFNWC HRW) - NOAA ATOVS data - brightness temperature (BT)( data of some MSG infrared channels Pre-processing: The CT and CTTH products (and their quality flags) are used to select the BT pixels to be assimilated. The pixels are kept over cloud-free areas and above those clouds, for which the cloud-top pressure levels are below the tail of the weighting functions. Test: The WV6.2, WV7.3, IR8.7, IR10.8, IR12.0 brightness temperatures were assimilated into the ALADIN/HU model. Operational since June 2009 Using only WV6.2 and WV7.3 channels together with SYNOP data (T2m and RH2m)
49 Automatic applications in the Hungarian nowcasting system (MEANDER) + warning system present applications - since 2005 plans: MEANDER - MEsoscale Analysis, Nowcasting and DEcision Routines CMa, CT for deriving cloud amount CT for filtering radar noises on cloud-free areas (+ very thin cirrus + ) CT for sending warning for potential foggy areas (using CT + RH analyses, derived low visibility) CTTH cloud top height (+ radar cloud top height + many other parameters) estimate the maximum wind speed in the thunderstorm outflow albedo and vegetation fraction (Land SAF) as input in WRF (forcasted CT) --- improve the fog module based on CT + using the Toulouse algorithm (RH, wind, prec.) --- using 6 hourly snow cover maps (CT + Land SAF Snow Cover product) to assimilate it into WRF model --- using RDT (with radar cell tracking) --- using Land Surface Temperature (Land SAF)
50 Creating Infra-cloud images for partners (e.g. Roads admin.) CT as cloud mask -- IR10.8 (black&white) on cloudy areas -- orographic map (colors) on cloud-free areas :55 UTC
51 Creating Infra-cloud images for partners (e.g. Roads admin.) CT as cloud mask -- IR10.8 (black&white) on cloudy areas -- orographic map (colors) on cloud-free areas :55 UTC
52 Surface chart
53 Automatic application - cloud amount map derived from the CT product How? - Creating a cloud mask from CT by setting 0 - cloud-free, broken clouds and very thin cirrus, 1 - other cloud types Smoothing/averaging this image with a 5x5 window :25 UTC Cloud Type
54 Automatic application - cloud amount map derived from the CT product How? - Creating a cloud mask from CT by setting 0 - cloud-free, broken clouds and very thin cirrus, 1 - other cloud types Smoothing/averaging this image with a 5x5 window Application: Verifying the forecasted cloud amount by ALADIN/HU (and other) NWP model :25 UTC Cloud amount
55 Using satellite data/ products at developing newer products Research verification Interactive applications case studies Automatic applications program
56 Thanks for my colleagues helping me! I. Szenyán Zs. Kocsis Thank you for your attention!
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PAGE : 1/11 TITLE: Product Specification Authors : P. Wang, R.J. van der A (KNMI) PAGE : 2/11 DOCUMENT STATUS SHEET Issue Date Modified Items / Reason for Change 0.9 19.01.06 First Version 1.0 22.01.06
WxFUSION A. Tafferner iport Meeting 13.10.2010 @ DLR OP Folie 1 Upper Danube Catchment MUC DLR Andechs Munich Vienna Folie 2 Local and propagating thunderstorms in the Upper Danube Catchment B. Barternschlager,
Use of ARM/NSA Data to Validate and Improve the Remote Sensing Retrieval of Cloud and Surface Properties in the Arctic from AVHRR Data X. Xiong QSS Group, Inc. National Oceanic and Atmospheric Administration
Cloud Classification schemes 1) classified by where they occur (for example: high, middle, low) 2) classified by amount of water content and vertical extent (thick, thin, shallow, deep) 3) classified by