Precipitation - Event Week 2015

Radars provide the most direct means of remotely measuring precipitation over large areas. The value of ground-based radars for indicating the location and intensity of rainfall was recognized shortly after the first surveillance radars were developed during World War II. Since then radar networks have been widely used to estimate precipitation accumulations, track storms, and provide valuable information to the public. However, vast areas of the Earth including oceanic, mountainous, and densely forested regions are largely inaccessible to ground-based instruments. As a result, much of our knowledge of precipitation in such areas derives from passive satellite measurements that can suffer from uncertainties owing to the indirect relationship between radiation measured at the satellite and rainfall at the surface. Satellite precipitation radars offer some of the benefits of their ground-based counterparts with the enhanced coverage afforded by orbiting the Earth. To date three such radars have been launched providing a new perspective of global precipitation from tropical rainfall to polar snows that complements the longer-term record provided by passive instruments. This lecture will describe the physical principles used to relate the reflectivities measured by these radars to precipitation intensity. We will examine the main features of the associated precipitation retrieval algorithms and some of the important factors that need to be considered will be highlighted through illustrative examples. We will conclude by contrasting the strengths and limitations of satellite radars against passive satellite sensors and ground-based radar systems.

As reported at the AMS annual meeting of 2015, a prototype system was developed for the second generation CMORPH to produce global analyses of 30-min precipitation on a 0.05olat/lon grid over the entire globe from pole to pole through integration of information from satellite observations as well as numerical model simulations. The second generation CMORPH is built upon the Kalman Filter based CMORPH algorithm of Joyce and Xie (2011). Inputs to the system include rainfall and snowfall rate retrievals from passive microwave (PMW) measurements aboard all available low earth orbit (LEO) satellites, precipitation estimates derived from infrared (IR) observations of geostationary (GEO) as well as LEO platforms, and precipitation simulations from numerical global models. Key to the success of the 2nd generation CMORPH, among a couple of other elements, are the development of a LEO-IR based precipitation estimation to fill in the polar gaps and objectively analyzed cloud motion vectors to capture the cloud movements of various spatial scales over the entire globe. In this presentation, we report our recent work on the LEO-IR based precipitation estimation.
The prototype algorithm for the LEO IR precipitation estimation is refined to achieve improved quantitative accuracy and consistency with PMW retrievals. AVHRR IR TBB data from all LEO satellites are first remapped to a 0.05olat/lon grid over the entire globe and in a 30-min interval. Temporally and spatially co-located data pairs of the LEO TBB and inter-calibrated combined satellite PMW retrievals (MWCOMB) over the tropics and mid latitudes and CloudSat radar precipitation over high latitudes and polar regions are then collected to construct tables. Precipitation at a grid box is derived from the TBB through matching the PDF tables for the TBB and the MWCOMB/Cloudsat-precipitation. This procedure is implemented for different season, latitude band and underlying surface types to account for the variations in the cloud – precipitation relationship.
Quantitative experiments are conducted to optimize the LEO IR based precipitation estimation technique using MWCOMB/CloudSat.

The estimation of precipitation from space was attempted almost at the beginning of the satellite meteorology era by establishing a somewhat loose link between visible and infrared imagery of cloud tops and precipitation intensity at the ground. Since the early days estimation methods have qualitatively and quantitatively evolved with the advent of passive microwave sensors first and precipitation and cloud radars more recently. The purpose of the lecture is to provide a necessarily brief overview of the basic physical principles underlying satellite precipitation estimation methods trying to make the audience aware of what the sensors actually “measure” (radiation properties) and how these measurements are converted into precipitation intensity. All the methods, either based on “passive” or “active” sensing, are necessarily indirect and thus a clear understanding of the physics of radiation and of cloud hydrometeors is needed for the correct use of the products. In fact, such understanding helps in identifying the limitations of the existing precipitation products, which are too often used improperly or taken for granted. The lecture will try to pave the way to the in depth lectures of the other instructors on more specific topics of the discipline.

Polar-orbiting microwave sensor generated rain rate estimates are the most accurate satellite-derived estimates of rainfall because of their direct relationship between liquid and ice hydrometeors and surface precipitation. People may find that surprising because the timeliness, latency and spatial resolution are inferior to geostationary satellite rain estimates. But the primary advantage that microwave sensors aboard operational polar orbiters have over geostationary satellites is the ability to see through the clouds and capture what is below the cloud tops. The current operational polar-orbiting satellite sensors aboard the DMSP (SSMI/S sensor) and NOAA/METOP/S-NPP satellites (AMSU/MHS and ATMS sensors) that generate instantaneous areal average microwave rain rates will be presented and explained, along with the recent development of combining these measurements into a Blended Rain Rate Product and an additional blended product called QMORPH. In the future polar-orbiting microwave rain rates will be used as a calibration of the next generation of geostationary satellite-derived rain estimates. A few recent case studies of the best use of operational microwave rain rates will also be shown, as well as those from more research oriented missions, such as GCOM (AMSR-2 sensor) and GPM (GPI sensors) that are also used in operations.

The advancements in satellite rainfall observations over the past decade have opened new horizons in hydrological applications at global scale. Specifically, newly available high resolution (8-25 km km, 1-3 hourly) satellite precipitation estimates (HPE) have allowed researchers to consider their potential integration with hydrologic models for flood modelling applications. However, performance evaluation of HPEs in cases of major flash flood-inducing storms is needed to assess their ability to represent the high rainfall variability associated with these storms. Furthermore, derivation of error metrics are usually based on long time records (years) thus results are bulked and cannot provide clear evidence for the efficiency of high resolution satellite precipitation products in quantifying heavy precipitation events that are usually responsible for the occurrence of flash floods. In this talk we will review a comprehensive error analysis of the currently available global-scale HPE products based on a number of major flash flood-inducing storms that occurred in Southern Europe and Western Mediterranean basins over the past 12 years. Quality controlled rainfall datasets derived from high-resolution radar-rainfall estimates and/or dense rain gauge network observations are used for reference. The ability of satellite-rainfall to represent the magnitude and spatiotemporal patterns of each storm is examined. Strengths and limitations of each product are highlighted and general findings are anticipated to serve as a valuable reference to both hydrologists and satellite product developers. Finally, the error propagation from rainfall to flood simulation is examined, and error correction techniques based on a newly developed NWP-based correction technique are evaluated in terms of their impact on flood prediction efficiency.

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.

This lecture presents the concept, the validity, and the applicability, of the method developed at the Royal Netherlands Meteorological Institute (KNMI) to calculate precipitation occurrence and intensity from cloud physical properties retrieved from passive imager satellite data onboard geostationary satellites.
A brief introduction will be given on the concept of the cloud physical properties retrievals and the precipitation retrievals, as well as an underpinning why cloud physical properties are pre-eminently suitable for quantitative precipitation retrievals. The validity of the precipitation retrievals will be demonstrated for Europe (and Africa), using weather radar and gridded rain-gauge data to perform a triple-collocation statistical evaluation. For this evaluation, the precipitation retrievals are derived using the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard Meteosat Second Generation satellites, the weather radar data are obtained from the common European integrated weather radar system, and the gridded rain gauge observations are obtained from the Global Precipitation Climatology Centre (GPCC) and/or the European Climate Assessment and Data set (ECA&D). The spatial and temporal dependence of the respective errors are presented and discussed.
The results suggest that the gridded rain-gauge datasets agree very well with the precipitation retrievals from SEVIRI, while they agree weakly with the weather radar observations. Part of these differences is caused by the fact that the weather radar products are based on different radars and algorithms, whereas the precipitation retrievals from SEVIRI have the advantage to be based on a single instrument. Since observations from single weather radar can be used to determine temporal variations in precipitation it is concluded that these observations are best suited for studying the diurnal or seasonal variations at a local scale.
The lecture will conclude with some examples for using the precipitation retrievals from SEVIRI for specific weather and climate applications.

The Global Precipitation Measurement (GPM) mission is an international network of satellites that provide next-generation global observations of rain and snow. The GPM concept centers on the deployment of a GPM Core Observatory satellite (launched Feb 2014), which is a joint NASA/JAXA partnership. The GPM Core Observatoryoperates an advanced radar and radiometer system to measure precipitation from space and serves as a reference standard to unify precipitation measurements from a constellation of research and operational satellites.GPM has a unique role in providing datasets for science and societal applications related to the Earth's water cycle at both regional and global scales and over long time periods if one includes the 17-year record of precipitation from the Tropical Rainfall Measuring Mission (TRMM) along with the expected 10 years from GPM. GPM is a mission with both scientific and application goals and as such has both high-quality research data products and near real-time (NRT) data products. The NRT products are released 1-6 hours after data collection and are important for operational users and weather related disaster applications. Some products are every 30 minutes and at a 0.1deg by 0.1deg (~10km by 10km) footprint. The research products are used for scientific research and climatology and weather/climate models. During this lecture information will be presented on how the satellite instruments work, the retrievals algorithms perform, the data is used for scientific investigations and societal applications such as floods, landslides, cyclones, and how to obtain GPM datasets.

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.

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.

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/infrared) 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 thesevere 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.