Practical Guide

 

Introduction

This guide explains the NWC SAF products from a practical point of view, when used in a forecasting environment. It should be useful for forecasters and end users. The main objective of this guide is to highlight the main characteristics of each product and its usefulness, as well as its limitations. Each product is illustrated with an example that summarizes the highlights of the product.

 

Cloud Mask

Developed by Météo-France in the frame of NWCSAF

Description

Brief description and principal goals of the product:

• To detect the presence of dust clouds.

• To detect the presence of volcanic ash.

Applicability

Main temporary and geographical location where the product is useful and available. In which time range is this product useful? How often is the product generated? How is it disseminated?

• Fairly reliable for MSG.

• Not available in case low solar elevation; dust cloud not available at nighttime over land.

• At the temporal frequency of the satellites.

Validation

Is the product adequately validated?

Only limited validation is available:.

• Dust clouds validated using a manually-gathered database (covering Africa and adjacent seas).

• Volcanic ash only subjectively checked on selected events.

What to look for?

Which features of the product do you believe are outstanding for nowcasting or other meteorological applications?

As quality is not perfect, a temporal animation or a synthesis over a few hours may help to identify the areas covered by dust or volcanic ash.

Limitations

What are the main limitations of the product?

Dust cloud:

• Over land, dust layer not detected at nighttime or in case low solar elevation.

• Thin dust layer not detected (especially over land).

Volcanic ash:

• Not available in case low solar elevation.

• Quality of detection depends on the volcanic events.

Additional information

Links to web pages where the product or its usefulness is detailed:

Practical info and documentation

Scientific and Validation report for the Cloud Product Processors of the NWC/GEO

Additional Information

Algorithm description

Explanation of the main aspects of the products and how it is generated.

Algorithm based on multispectral tests.

Test cases

Example of volcanic ash temporal synthesis (15h-8h UTC) (chilean volcano Puyehue, June 2011):

Example of dust cloud temporal synthesis (3h-21h UTC):

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Cloud Type

Developed by Météo-France in the frame of NWCSAF

Description

Brief description and principal goals of the product:

• To identify cloudy and cloud free areas.

• To detect the presence of snowy grounds.

• To identify major cloud classes (low broken (fractional), high semi-transparent, high, mid-level and low thick clouds).

Applicability

Main temporary and geographical location where the product is useful and available. In which time range is this product useful? How often is the product generated? How is it disseminated?

• Available from meteorological geostationary satellites (MSG),

• during day and night and

• at the temporal frequency of the satellites.

Validation

Is the product adequately validated?

• Cloud-free and cloudy areas are validated using SYNOP/SHIP observations.

• Major cloud classes are validated using space-born CALIOP lidar.

What to look for?

Which features of the product do you believe are outstanding for nowcasting or other meteorological applications?

An animation of CT allows to analyse the meteorological situation and help to visually identify:

• Convection.

• Low clouds/fog.

Limitations

What are the main limitations of the product?

Dust cloud:

• Low clouds may not be detected at low sun elevation or even at night-time (infrequent).

• Snow is not identified at night-time and may be confused as low clouds.

• Low clouds surmounted by thin cirrus may be classified as mid-level clouds.

• Very low clouds may be classified as mid-level clouds in case strong thermal inversion.

• Very thin cirrus may be classified as low broken clouds.

Additional information

Links to web pages where the product or its usefulness is detailed:

Practical info and documentation

Scientific and Validation report for the Cloud Product Processors of the NWC/GEO

Additional Information

Algorithm description

Explanation of the main aspects of the products and how it is generated.

• Mainly based on multispectral tests.

• NWP field (surface temperature, atmospheric integrated water vapor content, air temperature on a few pressure levels) required to ensure good quality product.

Test cases

Atsani typhoon (Himawari8; 23 August 2015 2h)

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Cloud Top Temperature and Height

Developed by Météo-France in the frame of NWCSAF

Description

Brief description and principal goals of the product:

• To estimate the cloud top height (altitude in km or pressure in hPa).

• To estimate the cloud top temperature.

Applicability

Main temporary and geographical location where the product is useful and available. In which time range is this product useful? How often is the product generated? How is it disseminated?

• Available from meteorological geostationary satellites (MSG),

• during day and night and

• at the temporal frequency of the satellites.

Validation

Is the product adequately validated?

Is fully validated using space-born CALIOP lidar and CPR radar.

What to look for?

Which features of the product do you believe are outstanding for nowcasting or other meteorological applications?

Useful to estimate the height of convective clouds .

Limitations

What are the main limitations of the product?

• CTTH is not retrieved for low broken clouds.

• CTTH may be not retrieved for thin cirrus clouds.

• Retrieved low cloud top height may be overestimated.

Additional information

Links to web pages where the product or its usefulness is detailed:

Practical info and documentation

Scientific and Validation report for the Cloud Product Processors of the NWC/GEO

Additional Information

Algorithm description

Explanation of the main aspects of the products and how it is generated.

• Mainly based on comparison between satellite measurements and infrared-red simulations (RTTOV applied to NWP fields).

• NWP fields (temperature and humidity vertical profile) required for RTTOV simulations.

Test cases

Atsani typhoon (Himawari8; 23 August 2015 2h)

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Cloud Microphysics

Developed by Météo-France in the frame of NWCSAF

Description

Brief description and principal goals of the product:

CMIC allows to describe cloud microphysics:

• Thermodynamic phase at top of cloud.

• Water droplet/ice crystal size at top of cloud.

• Cloud optical thickness (COT).

• Liquid and Ice water path (IWP and LWP).

Applicability

Main temporary and geographical location where the product is useful and available. In which time range is this product useful? How often is the product generated? How is it disseminated?

• Available from meteorological geostationary satellites (MSG),

• during day and night (except thermodynamic phase available also at nighttime) and

• at the temporal frequency of the satellites.

Validation

Is the product adequately validated?

CMIC is partially validated:

• cloud thermodynamic phase with space-born CALIOP lidar.

• LWP with passive microwave imagery (AMSR).

What to look for?

Which features of the product do you believe are outstanding for nowcasting or other meteorological applications?

Useful for convection:

• Ice thermodynamic phase indicates glaciation of top of cloud.

• Large COT/IWP indicates thickest part of clouds (heavy rain).

Limitations

What are the main limitations of the product?

• CMIC not available for low broken clouds.

• Droplet/crystal size, COT and LWP/IWP not retrieved at nighttime and in case mixed/undefined phase or too tin clouds.

Additional information

Links to web pages where the product or its usefulness is detailed:

Practical info and documentation

Scientific and Validation report for the Cloud Product Processors of the NWC/GEO

Additional Information

Algorithm description

Explanation of the main aspects of the products and how it is generated.

Algorithm main features:

• Thermodynamic phase retrieved by thresholds tests.

• COT and droplet/crystal size retrieved by comparison of solar bands measurements and simulations.

• IWP and LWP retrieved from COT and droplet/crystal size.

Test cases

Atsani typhoon (Himawari8; 23 August 2015 2h)

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Convective Rainfall Rate and Convective Rainfall Based on Cloud Physical Properties

Developed by AEMET in the frame of NWCSAF

Description

Brief description and principal goals of the product:

• CRR and CRR-Ph provide an estimation of the rainfall rate in convective situations.

• The purpose of these products is to help the forecaster in the detection and tracking of convective systems with high rain rates.

Applicability

Main temporary and geographical location where the product is useful and available. In which time range is this product useful? How often is the product generated? How is it disseminated?

• Available over the geostacionary satellite coverage (MSG-0 degree, MSG Indian Ocean area).

• PC available during both day and night-time.

• PC-Ph available only during day-time.

• It is generated every 15 minutes.

Validation

Is the product adequately validated?

Validation of precipitation is problematic due to the double penalty problem. Subjective validation shows that both CRR and CRR-Ph are able to detect convective precipitation, being more accurate CRR-Ph.

What to look for?

Which features of the product do you believe are outstanding for nowcasting or other meteorological applications?

To look for the areas with the heaviest precipitation.

Limitations

What are the main limitations of the product?

• CRR tends to overestimate the precipitation area and underestimate the rain rates.

• CRR-Ph is not available during night-time.

• Although both have been calibrated for convective situations, they are generated in the whole image, so the forecaster has to previously detect in which areas there is convection and only trust the product in those areas.

Additional information

Links to web pages where the product or its usefulness is detailed:

Practical info and documentation

Algorithm description

Explanation of the main aspects of the products and how it is generated.

• CRR is generated from MSG SEVIRI data.

• CRR-Ph is generated from the NWCSAF CMIC product.

Test cases

• CRR

• CRR-Ph

• Radar

 

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Precipitating Clouds and Precipitation Products based on Cloud Physical Properties

Developed by AEMET in the frame of NWCSAF

Description

Brief description and principal goals of the product:

• PC and PC-Ph provide an estimation of the probability of precipitation.

• The purpose of this product is to detect areas with certain probability of precipitation occurrence.

Applicability

Main temporary and geographical location where the product is useful and available. In which time range is this product useful? How often is the product generated? How is it disseminated?

• Available over the geostacionary satellite coverage (MSG-0 degree, MSG Indian Ocean area).

• PC available during both day and night-time.

• PC-Ph available only during day-time.

• It is generated every 15 minutes.

Validation

Is the product adequately validated?

Validation of precipitation is problematic due to the double penalty problem. Subjective validation shows that both PC and PC-Ph are able to detect precipitation, being more accurate PC-Ph.

What to look for?

Which features of the product do you believe are outstanding for nowcasting or other meteorological applications?

To look for the areas with the high probability of precipitation.

Limitations

What are the main limitations of the product?

• PC does not detect precipitation from low clouds.

• PC-Ph is not available during night-time.

Additional information

Links to web pages where the product or its usefulness is detailed:

Practical info and documentation

Algorithm description

Explanation of the main aspects of the products and how it is generated.

• PC is generated from MSG SEVIRI data combined with NWP data.

• PC-Ph is generated from the NWCSAF CMIC product.

Test cases

• Example 1: PC vs. Radar

• Example 2: PC vs. Radar

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imaging Satellite Humidity And Instability product

Developed by AEMET in the frame of NWCSAF

Description

Brief description and principal goals of the product:

iSHAI provides:

• An estimation of precipitable water (in total column and in three layers), stability indices, total ozone and skin temperature on clear air pixels or boxes of N x N pixels (where N is configurable).

• The differences between the above retrieved fields and the ones calculated from vertical, temporal and spatially collocated profiles from a background NWP model.

The main purposes of iSHAI are:

• To help forecasters in the detection and tracking (with high spatial and temporal resolution) of key ingredients in convection to support real time meteorological applications; especially in pre-convective situations.

• To advice forecasters of the discrepancies between the background NWP and the retrieved fields.

Applicability

Main temporary and geographical location where the product is useful and available. In which time range is this product useful? How often is the product generated? How is it disseminated?

• In the current version, the product can be generated over a configurable region inside the coverage of MSG satellites (operational MSG at 0º degree, Rapid-Scan MSG at 9.5°E or Indian Ocean MSG at 41.5°) for pixels with satellite zenith angle below 70°.

•• In next versions, iSHAI could be generated for other geostationary satellites supported by the NWC/GEO software package. In version 2018, Himawari and GOES-R (TBC). MTG-FCI in future versions.

• iSHAI is available during both day and night-time.

• iSHAI can be generated for every new satellite image (every 15 minutes; 5 minutes in the case of Rapid-Scan).

• The iSHAI product is generated on boxes of N x N pixels (where N is configurable by the users). The size of the default box is 3x3; but can be generated in 1x1.

• iSHAI product is generated locally by the users executing the GEO-iSHAI program. This allows use of local NWP model as the NWP background.

•• Also ECMWF GRIB files in hybrid levels could be used as NWP background input to improve the vertical resolution.

Validation

Is the product adequately validated?

• iSHAI has been validated against ECMWF analysis and compared the errors and bias of every field with the ones of t+12 forecast of ECMWF for the full disc of MSG satellite. iSHAI fields are inside the “optimal accuracy threshold” defined for validation. See the iSHAI Validation report.

 

• iSHAI can also be compared subjectively; as example through the use of the difference fields output.

What to look for?

Which features of the product do you believe are outstanding for nowcasting or other meteorological applications?

iSHAI is useful for nowcasting applications (especially in pre-convective situations):

• Watch and warning of pre-convective situations through the monitoring of the evolution of several key ingredients in convection.

• Monitoring of the humid atmospheric flow. This allows the monitoring of the humidity convergence/divergence in clear region on pre-convective situations.

• Advice the evolution and regions with instability.

• Since iSHAI is generated locally by the users, it can be activated also the generation of binary files with temperature, humidity and ozone profiles at the retrieved and background NWP steps. This allows users to calculate other precipitable water layers or stability indices; also the profiles could be exploit in advanced 2D and 3D displays.

•• In the case when the background model is the ECMWF in hybrid levels, the dense vertical resolution allows a 3D comparison through vertical cross sections or interactive sounding displays.

Limitations

What are the main limitations of the product?

• The main limitation is that is not available on cloudy pixels. Once the clouds develop only information from neighborhood is available.

• Droplet/crystal size, COT and LWP/IWP not retrieved at nighttime and in case mixed/undefined phase or too tin clouds.

•• The cloud mask near edge of clouds tends to produce larger differences with the background NWP perhaps due to cloud contaminated pixels.

• MSG satellites have few IR and WV channels, not too much information to modify the background NWP profile.

• The main errors are related to disagreement between the background NWP model used and the true atmosphere. It is recommend to use profiles with as much spatial, temporal and vertical resolution (use of enough pressure levels) as possible.

• The iSHAI product is generated on boxes of N x N pixels (where N is configurable by the users). The size of the default box is 3x3; but can be generated in 1x1. The execution time of iSHAI could be huge for large regions if processed with N too low.

Additional information

Links to web pages where the product or its usefulness is detailed:

iSHAI Product User Manual

Practical info and documentation

iSHAI bibliography

Algorithm description

Explanation of the main aspects of the products and how it is generated.

The iSHAI algorithm is a combination of one statistical and one optimal estimation algorithms. It has two main steps:

• In the first step, the iSHAI First Guess profile is built using a set of non-linear regressions from vertical, temporal and spatially collocated profiles from a background NWP model and bias corrected SEVIRI BTs.

• In the second step, a physical retrieval algorithm (in EOFs space to speed up the process) is applied when the distance between BTSEVIRI and synthetic BTRTTOV is greater than a configurable threshold.

Test cases

• In the Figure below, it is shown over the availability time line the images related with precipitable water in the Middle Layer ML (850-500 hPa) field for the case study of 20th June 2013 when strong convection took place in Germany. It can be seen that the former PGE13 in the 12:00 Z image allows to detect that the ML field forecasted for the ECMWF in the 00Z run is wrong and more precipitable water than forecasted is available in a highly convective situation; this is confirmed several hours later by the ECMWF 12:00Z analysis.

• The complete case study and more examples of the use of iSHAI and former PGE13 SPhR product are available in the presentation in the Martinez presentation in NWC SAF Workshop 2015.

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High Resolution Winds

Developed by AEMET in the frame of NWCSAF

Description

Brief description and principal goals of the product:

• NWC SAF/HRW calculates detailed dataset of “Atmospheric Motion Vectors (AMVs)” and “Trajectories” throughout all layers of the Troposphere.

• “AMVs” are the horizontal displacement of cloudiness and moisture features (called “tracers”) between two Earth positions in two satellite images, and are associated with the horizontal wind in the atmosphere.

• The “Trajectories” define consecutive positions of these tracers, throughout several consecutive satellite images.

• The main purpose of NWC SAF/HRW is to provide detailed observations of horizontal wind data, with global coverage at regular interval times. These observations can be very important for weather analysis and forecast tasks, especially over oceans and remote areas, in which other sources of wind data are scarce.

Applicability

Main temporary and geographical location where the product is useful and available. In which time range is this product useful? How often is the product generated? How is it disseminated?

• NWC SAF/HRW is available for the whole coverage areas of MSG and GOES-N geostationary satellite series. It will also be available for the whole coverage areas of Himawari-8/9 and GOES-R geostationary satellite series since next version v2018.

• HRW can be generated for every new satellite image, considering tracers defined in infrared/water vapour images (during daytime and nighttime) and in visible images (during daytime).

Validation

Is the product adequately validated?

• NWC SAF/HRW AMVs are validated against rawinsonde winds for both MSG and GOES-N satellite series. They are inside the “optimal accuracy threshold” defined for validation, considering all atmospheric layers together (NRMSVD < 0.40).

 

• HRW AMVs have also been comparatively validated in 2014 with other AMV data produced worldwide (by EUMETSAT, NOAA, CPTEC/INPE, CMA, JMA, KMA). The study shows that HRW AMVs have in general the best statistics: link

• The WMO/CGMS (Coordination Group for Meteorological Satellites) has also recognized in its 2012 and 2015 Reports: link1 (page 117) and link2 (page 152)

•• NWC SAF/HRW software fulfills requirements to be a portable standalone AMV calculation software due to its easy installation and usability.

•• A continuous exchange of information about usability and improvements of the software exists through the NWCSAF Helpdesk.

•• Although alternatives exist as portable standalone AMV calculation software, they are not as advanced in terms of documentation and do not have an existing Helpdesk.

•• Appreciation is expressed to the NWC SAF for preparing HRW software, for helping overcome obstacles in the use of AMVs by both operational and research users, and for its contribution to the harmonisation of AMV products.

•• The software has been successfully adapted by some CGMS members; they express that HRW is an important tool for development, modular, well documented, and well suited as “standalone AMV software”.

•• NWCSAF is among the two centres obtaining the best validation statistics for its AMVs.

• Considering all this, the product is fully usable without any restrictions.

What to look for?

Which features of the product do you believe are outstanding for nowcasting or other meteorological applications?

• NWC SAF/HRW is useful for nowcasting applications:

•• Watch and warning of dangerous wind situations.

•• Monitoring of the general atmospheric flow, small scale circulation or wind singularities.

•• Monitoring of the wind convergence/divergence (related to the development/dissolution of meteorological systems).

• NWC SAF/HRW can also be assimilated as wind data in Numerical Weather Prediction Models, or other Analysis and Forecast applications (together with many other data).

Limitations

What are the main limitations of the product?

• The main limitation of NWC SAF/HRW is the variability with time of the amount of AMVs and Trajectories, through which there can be areas with no HRW data. This is related to the presence and evolution with time of cloudiness/moisture features.

• The situation improves with the latest versions of NWC SAF/HRW algorithm, with the option to calculate AMVs and Trajectories with more satellite channels, and the option since next v2018 to use satellite data with a better spatial and temporal resolution, providing a larger amount of HRW data.

• About the calculated AMVs and Trajectories, the main errors are related to inconsistencies between the NWP model used for the calculation and the true atmosphere.

Additional information

Links to web pages where the product or its usefulness is detailed:

Practical info and documentation

Atmospheric Motion Vectors & Trajectories through the displacement of features in Geostationary meteorological satellite images

Algorithm description

Explanation of the main aspects of the products and how it is generated.

NWC SAF/HRW algorithm is based on next steps:

• The definition of tracers in an initial image.

• The tracking of these tracers in a later image.

• The calculation of the AMVs and Trajectories through the displacement of these tracers between the two images.

• The height assignment, to locate these AMVs and Trajectories vertically in the Troposphere.

• A Quality Control process, to remove poor quality data.

NWC SAF/HRW algorithm uses as input data:

• Visible/infrared/water vapour satellite images.

• NWP forecast fields for temperature, wind, geopotential.

• NWC SAF/Cloud products: CT, CTTH, CMIC.

Test cases

• Case of “Rapid Cyclogenesis Xynthia” in France on 27th-28th Feb. 2010 is shown.

• NWC SAF/HRW storm force AMVs (higger than 89 km/h) show clearly in real time the areas affected by the strongest winds, as later verified by surface wind observations by Météo France.

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Rapidly Developing Thunderstorm

Developed by AEMET in the frame of NWCSAF

Description

Brief description and principal goals of the product:

RDT detects tracks characterizes and forecasts convective cells.

Applicability

Main temporary and geographical location where the product is useful and available. In which time range is this product useful? How often is the product generated? How is it disseminated?

• Available over the geostationary satellite coverage.

• Need at least satellite input data refreshed every 30'.

• Forecast up to 1h.

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Validation

Is the product adequately validated?

• Objective and subjective validation over Europe, subjective validation outside Europe, some objective validation by end-users.

 

• The product fulfill the Eumetsat requirements and has the status “operational”.

• POD higger than 70%, early diagnosis in 25% of cases.

What to look for?

Which features of the product do you believe are outstanding for nowcasting or other meteorological applications?

RDT is made to satisfy several kinds of end-users concerned by convection. For example:

• Aeronatical end-users: overshooting tops, severity, 2D contours, TOP estimate, +1h forecasts, cooling rate, etc.

• Forecaster: tracking, forecasts, cooling rate, etc.

• Warning systems for ground-based end-users: 2D contours, rainfall attribute, lightning activity, etc.

Limitations

What are the main limitations of the product?

• Warm system detection and convection diagnosis for these systems.

• Forecast: no creation or dissipation of cells, no change in trajectory.

• Performances better in summer or intermediate season compared to winter.

Additional information

Links to web pages where the product or its usefulness is detailed:

Scientific Documentation

General Information

Algorithm description

Explanation of the main aspects of the products and how it is generated.

• The object-oriented approach underlying the RDT product allows to add value to the satellite image by characterizing convective, spatially consistent, entities through various parameters of interest. The most sensitive part of the algorithm is the discrimination process (to distinguish convective cells from the other ones’)

• Input Data: satellite BT, other NWCSAF products (CT Cma CMIC CRR HRW), NWP parameters, lightning data.

Test cases

• On 25th May 2009 (“Topical case” situation), convective cells are growing and merging in the south-west of Germany between 11h and 17h UTC. They are early depicted and diagnosed as convective by RDT, even if those cells are already well developed (minimum temperatures of the cells are cold).

• The early diagnosis varies from 0 to 90 minutes for individual cells, but the value to retain is 45 minutes between first diagnosed cell and first paired flash in the neighbourhood. The first cell is diagnosed at 11h15 UTC, 30 minutes after its first detection, but 90 minutes before a lightning flash be paired with this cell. But the first paired flashes really appear at 12h UTC. All those cells finally merge together, and dissipate around 16h45. The lighting activity ends at 15h15.

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Convection Initiation

Developed by Météo-France in the frame of NWCSAF

Description

Brief description and principal goals of the product:

CI provides an estimation of the probability for a low cloud to develop as a thunderstorm in the next 0-30’, 0-60’ or 0-90’ interval.

Applicability

Main temporary and geographical location where the product is useful and available. In which time range is this product useful? How often is the product generated? How is it disseminated?

• Available over the geostationary satellite coverage

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Validation

Is the product adequately validated?

v2016 has the status “demonstrational” and validation will be extended in next version (v2018).

 

What to look for?

Which features of the product do you believe are outstanding for nowcasting or other meteorological applications?

To define pixels or areas of possible convection. Usefull for early warning systems and forecasters.

Limitations

What are the main limitations of the product?

• In cases of cirrus above low clouds (no detection).

• High FAR inherent to CI

• Improvement expected in v2018.

• Lack of validation.

Additional information

Links to web pages where the product or its usefulness is detailed:

Scientific Documentation

General Information

Algorithm description

Explanation of the main aspects of the products and how it is generated.

The process follows:

• The detection of cloud systems.

• The tracking of cloud systems.

• The discrimination of convective cloud objects.

• The advection of convective cloud objetcs.

Input Data: satellite BT, other NWCSAF products (CT Cma HRW), NWP parameters.

Test cases

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Automatic Satellite Image Interpretation

Developed by ZAMG in the frame of NWCSAF

Description

Brief description and principal goals of the product:

• ASII provides an analysis of the satellite image in terms of conceptual models (e.g. cold fronts, warm fronts, occlusions, etc.).

• ASII comes with two operational modes: An analysis of conceptual models from image data alone, and one with supplementary NWP data.

Applicability

Main temporary and geographical location where the product is useful and available. In which time range is this product useful? How often is the product generated? How is it disseminated?

• ASII is available day and night on the MSG full disk area.

• High viewing angles at the edge of the satellite image have a limiting impact on the recognizability of conceptual models.

Validation

Is the product adequately validated?

• ASII has been extensively validated with different datasets: Algorithm Theoretical Basis Document for "Automatic Satellite Image Interpretation"

 

• The performance of the product is satisfactory in view of the limited number of conceptual models being detected. Complex synoptic situations with a high number of different conceptual models not well separated from each other are challenging to the ASII analysis.

 

What to look for?

Which features of the product do you believe are outstanding for nowcasting or other meteorological applications?

ASII provides a basic analysis of the satellite image data in terms of conceptual models. This analysis is done much faster than a meteorologist could do.

Limitations

What are the main limitations of the product?

• Although available on the full disk, this product has been developed to detect conceptual models prevailing in the mid-latitudes of the northern hemisphere. Tropical systems might be detected but mostly labeled incorrectly.

• ASII detects only a limited number of conceptual models. This may lead to the misinterpretation of cloud systems which are not in its portfolio.

Additional information

Links to web pages where the product or its usefulness is detailed:

Algorithm Theoretical Basis Document for "Automatic Satellite Image Interpretation"

Algorithm description

Explanation of the main aspects of the products and how it is generated.

• ASII performs the satellite image interpretation by applying various image processing algorithms essentially to the IR 10.8 µm and the WV 6.2 µm image data.

• From this and auxiliary data like Atmospheric Motion Vectors and NWP data, the conclusion about the presence of a conceptual model is done by a decision-tree scheme.

Test cases

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Automatic Satellite Image Interpretation - New Generation

Developed by ZAMG in the frame of NWCSAF

Description

Brief description and principal goals of the product:

• ASII-NG detects pattern in the satellite imagery which are associated with turbulence in the atmosphere.

• Currently, it aims at detecting tropopause foldings from a combination of satellite and NWP input (yet there is more to come in the future).

• The product output consists of values of probability of occurrence (0-100%).

Applicability

Main temporary and geographical location where the product is useful and available. In which time range is this product useful? How often is the product generated? How is it disseminated?

• ASII-NG is available day and night over the geostationary disk (MSG-0 degree, MSG Indian Ocean area, GOES-N).

• The tropopause foldings product is useful all over the disk.

Validation

Is the product adequately validated?

The product has not yet undergone a decent validation.

 

What to look for?

Which features of the product do you believe are outstanding for nowcasting or other meteorological applications?

This product provides an analysis of tropopause folds pone to the occurrence of turbulence from a combination of satellite and NWP data. It gives to the meteorologist a quick and comprehensive overview of the affected areas.

Limitations

What are the main limitations of the product?

Tropopause foldings are only one of several sources for the occurrence of in-flight turbulence. The meteorologist in duty needs to monitor other sources of turbulence as well.

Additional information

Links to web pages where the product or its usefulness is detailed:

Practical info and documentation

Algorithm description

Explanation of the main aspects of the products and how it is generated.

• The ASII-NG tropopause foldings product uses logistic regression to combine the input parameters.

• The tropopause foldings product uses NWP data besides the satellite data.

Test cases

Turbulence reported over area of Black Sea and Caspian Sea

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Extrapolated Imagery

Developed by ZAMG in the frame of NWCSAF

Description

Brief description and principal goals of the product:

• Calculation of forecast satellite or NWC SAF output images in the working region, through the extrapolation with atmospheric motion vectors.

• The purpose of these products is to provide the forecaster with the most likely appearances of the images over the coming hour.

Applicability

Main temporary and geographical location where the product is useful and available. In which time range is this product useful? How often is the product generated? How is it disseminated?

• Available over the geostationary disk (MSG-0 degree, MSG Indian Ocean area, GOES-N).

• Available during both day- and night-time (unless the input to the extrapolation has constraints in this respect).

Validation

Is the product adequately validated?

The validation of EXIM forecast images/products is carried out against the satellite images (or generated NWC SAF outputs) actually obtained at the forecast date. The extrapolation should beat persistence forecast, and it normally does.

 

What to look for?

Which features of the product do you believe are outstanding for nowcasting or other meteorological applications?

To look for the same phenomena as in the satellite imagery and in the NWC SAF products, only at a future date.

Limitations

What are the main limitations of the product?

Pure kinematic extrapolation necessarily fails to model new meteorological developments, sunrise/sunset in extrapolated visible imagery…

Additional information

Links to web pages where the product or its usefulness is detailed:

Practical info and documentation

Algorithm description

Explanation of the main aspects of the products and how it is generated.

• Derive a pixel-fine displacement field from the HRW product.

• Displace the pixels along their associated trajectories.

Test cases

• CMA at 08:15

• EXIM. Extrapolated product from 08:15 to 09:15

• CMA at 09:15

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