Identify and interpret fields and derived products

The Satellite Application Facility for supporting operational hydrology and water management (H-SAF), established in 2005 as part of the EUMETSAT SAF network, is designed to provide the user community with new satellite-derived products from existing and future satellites with sufficient time and space resolution to satisfy the needs of operational hydrology. Three hydrological variables are considered(soil moisture, snow at the ground and precipitation) and a number of related parameters are made available to the user community, with a quantitative description of their accuracy.
Precipitation products are derived from algorithms based on different satellite data (active and passive microwave, visible/infra-red) and approaches (artificial neural network, Bayesian statistics, pattern recognition) to provide the most advanced set of precipitation product over Europe, and, in the next future, over Africa. The use of H-SAF precipitation products to study the severe meteorological events occurred in Italy in the autumn 2014 showed their potential as additional tools in monitoring heavy rainfall, especially in cases when conventional, ground-based instruments are not able to fully describe the precipitation pattern and intensity.

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Lecture slides

Anke Thoss starts her presentation with some thoughts on satellite derived rain rates and rain probabilities from LEO and GEO satellites. She points out that satellite derived rainfall products are especially helpful in regions without radar coverage. The precipitating cloud product is presented with an outline of the product algorithm. She demonstrates the product which is visualized in RGB colours reflecting the likelihood of instantaneous precipitation classes. A product validation based on version 2008 (product unchanged since then) is presented. The presentation ends with a look into the future, the MetOp second generation and its impact on the precipitation product.

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EUMETSAT satellite derived or satellite-NWP combined precipitation estimators like Multi-Sensor Precipitation Estimate (MPE) and Nowcasting SAF Convective Rainfall Rate (PGE05 CRR) or Cloud Physical Properties Convective Rainfall Rate (PGE14 CRPh) are best used for precipitation diagnostic and analysis in convective environment. Full benefit of those products is best demonstrated in the regions with none or poor meteorological radars coverage. As with any satellite derived estimator/product, there are both benefits and limitations.
Here we will focus on Nowcasting SAF precipitation products derived from EUMETSAT MSG SEVIRI BS satellite data with the aid of numerical weather predictions from ECMWF and/or ALADIN models. Those products are being used operationally at DHMZ.

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In this lecture, I will focus on the rainfall in the tropical regions and I will present the multi-platforms precipitation products that are available. I will explain the functioning of the retrievals and will put the emphasis on the characterization of the errors and uncertainties associated with the satellite products. Time will be devoted to the introduction of the passive microwave Global Precipitation Measurements constellation with emphasis on the Megha-Tropiques mission. An effort will be made to showcase what the end user can expect from the products developed in many centers worldwide with examples from various validation campaigns. I will end the lecture with a brief presentation of the activity and available resources of the International Precipitation Working Group from WMO/CGMS.

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The Meteosat Third Generation Imaging satellite will include in addition to the Flexible Combined Imager (FCI), which is a continuation of the imaging mission, a Lightning Imager (LI) which is a completely new mission. The MTG LI will complement existing ground based capabilities for the detection and location of lightning with information on cloud-to-ground (CG) and cloud-to-cloud or intracloud (CC/IC) discharges, i.e. total lightning. The presentation will cover the background for lightning detection from space, including the detection and processing principles, and will concentrate on the meteorological products from the lightning imager. These include lightning flashes as well as accumulated lightning products.

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NASA’s Short-term Prediction Research and Transition (SPoRT) center has been a leader in transitioning total lightning observations from ground-based lightning mapping arrays (LMAs) to operational forecasters. This effort began in 2003 with a single, NASA-owned LMA and has since expanded to include multiple LMAs focusing on warning decision support, lightning safety, and aviation forecast needs. This presentation will cover SPoRT’s total lightning activities, focusing on operational applications both with the LMAs directly and in preparation for the GOES-R Geostationary Lightning Mapper.

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Croatian Adriatic coast is one of the rainiest areas in Europe. Large parts of the coast are not covered by the Croatian Radar network. The Croatian Weather Service (DHMZ) uses data from the NWC-SAF precipitation products to complement Radar data. Results from a validation campaign, using Radar and rain gauge data, will be shown in this presentation.

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The Rapidly Developing Thunderstorm product provides in object-mode information about significant convective systems. Various attributes describe the convective system: cloud-top temperature and height, overshooting tops, morphological attributes. The product uses the satellite brightness temperature of various channels and optional data like NWP data, lightning data and other NWCSAF products. A nowcast of convective systems is developed for 2016.
A new NWCSAF product (Convection Initiation – the probability of a cloudy pixel to become a thunderstorm) will be developed for 2016.

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The Convective Rainfall Rate (CRR) product, included in the NWCSAF/MSG software package, estimates rain rates on convective and to convection associated stratiform events. The current version of the package includes estimations of rain intensities through two different approaches. A detailed explanation of them will be presented. Also a comparison, focusing on the weakness and strength of the results provided by both algorithms, will be shown.

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Firstly, the characteristics of synoptic weather which convective cloud occurs over eastern Asian region will be mentioned and the thunderstorm monitoring system with radar data which KMA forecasters use will be also introduced. And then I will present our convective cases and explain them with satellite, radar and other NWP data.

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Delineating areas where convection is most/least likely to develop can be difficult, especially in the next 3-9 hours. Knowing how forecasts can extend the usefulness of SEVIRI products from observations to forecasts can be equally challenging. This talk discusses a new forecast tool that addresses a number of questions: How can SEVIRI products be used to monitor and predict areas that are becoming more/less supportive for development of convection? Will the details in the SEVIRI observations be retained by short-range forecast tool? How do the derived SEVIRI forecast products relate to standard satellite forecasting conventions? Can IR satellite observations still be useful after convection has begun and clouds have formed? Are the SEVIRI projections useful for monitoring NWP performance?

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Since 2004, EUMETSAT is deriving the Multi-Sensor Precipitation Estimate (MPE) product operationally. The MPE product retrieval is based on a blending technique that uses microwave information from SSMI-S instrument onboard DMSP-F16 polar satellite and infrared information from the Spinning Enhanced Visible and Infra Red Imager (SEVIRI) instrument onboard MSG geostationary satellites. It consists of a rain rate given every 15 minutes at MSG pixel resolution (3x3km2). Although the retrieval is not a state-of-the-art technique, the product has proven to be quite robust and valuable for deep convective precipitation detection. This presentation will present the MPE product and show some example of potential improvements for future SEVIRI precipitation product.

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