A Synergistic Use of a High-Resolution Numerical Weather Prediction Model and High-Resolution Earth Observation Products to Improve Precipitation Forecast

open20siThe Mediterranean region is frequently struck by severe rainfall events causing numerous casualties and several million euros of damages every year. Thus, improving the forecast accuracy is a fundamental goal to limit social and economic damages. Numerical Weather Prediction (NWP) models are currently able to produce forecasts at the km scale grid spacing but unreliable surface information and a poor knowledge of the initial state of the atmosphere may produce inaccurate simulations of weather phenomena. The STEAM (SaTellite Earth observation for Atmospheric Modelling) project aims to investigate whether Sentinel satellites constellation weather observation data, in combination with Global Navigation Satellite System (GNSS) observations, can be used to better understand and predict with a higher spatio-temporal resolution the atmospheric phenomena resulting in severe weather events. Two heavy rainfall events that occurred in Italy in the autumn of 2017 are studied—a localized and short-lived event and a long-lived one. By assimilating a wide range of Sentinel and GNSS observations in a state-of-the-art NWP model, it is found that the forecasts benefit the most when the model is provided with information on the wind field and/or the water vapor content.openLagasio, Martina; Parodi, Antonio; Pulvirenti, Luca; Meroni, Agostino N.; Boni, Giorgio; Pierdicca, Nazzareno; Marzano, Frank S.; Luini, Lorenzo; Venuti, Giovanna; Realini, Eugenio; Gatti, Andrea; Tagliaferro, Giulio; Barindelli, Stefano; Monti Guarnieri, Andrea; Goga, Klodiana; Terzo, Olivier; Rucci, Alessio; Passera, Emanuele; Kranzlmueller, Dieter; Rommen, BjornLagasio, Martina; Parodi, Antonio; Pulvirenti, Luca; Meroni, Agostino N.; Boni, Giorgio; Pierdicca, Nazzareno; Marzano, Frank S.; Luini, Lorenzo; Venuti, Giovanna; Realini, Eugenio; Gatti, Andrea; Tagliaferro, Giulio; Barindelli, Stefano; Monti Guarnieri, Andrea; Goga, Klodiana; Terzo, Olivier; Rucci, Alessio; Passera, Emanuele; Kranzlmueller, Dieter; Rommen, Bjor

A virtual centre at the interface of basic and applied weather and climate research

The Hans-Ertel Centre for Weather Research is a network of German universities, research institutes and the German Weather Service (Deutscher Wetterdienst, DWD). It has been established to trigger and intensify basic research and education on weather forecasting and climate monitoring. The performed research ranges from nowcasting and short-term weather forecasting to convective-scale data assimilation, the development of parameterizations for numerical weather prediction models, climate monitoring and the communication and use of forecast information. Scientific findings from the network contribute to better understanding of the life-cycle of shallow and deep convection, representation of uncertainty in ensemble systems, effects of unresolved variability, regional climate variability, perception of forecasts and vulnerability of society. Concrete developments within the research network include dual observation-microphysics composites, satellite forward operators, tools to estimate observation impact, cloud and precipitation system tracking algorithms, large-eddy-simulations, a regional reanalysis and a probabilistic forecast test product. Within three years, the network has triggered a number of activities that include the training and education of young scientists besides the centre's core objective of complementing DWD's internal research with relevant basic research at universities and research institutes. The long term goal is to develop a self-sustaining research network that continues the close collaboration with DWD and the national and international research community

Satellite and in situ observations for advancing global Earth surface modelling: a review

In this paper, we review the use of satellite-based remote sensing in combination with in situ data to inform Earth surface modelling. This involves verification and optimization methods that can handle both random and systematic errors and result in effective model improvement for both surface monitoring and prediction applications. The reasons for diverse remote sensing data and products include (i) their complementary areal and temporal coverage, (ii) their diverse and covariant information content, and (iii) their ability to complement in situ observations, which are often sparse and only locally representative. To improve our understanding of the complex behavior of the Earth system at the surface and sub-surface, we need large volumes of data from high-resolution modelling and remote sensing, since the Earth surface exhibits a high degree of heterogeneity and discontinuities in space and time. The spatial and temporal variability of the biosphere, hydrosphere, cryosphere and anthroposphere calls for an increased use of Earth observation (EO) data attaining volumes previously considered prohibitive. We review data availability and discuss recent examples where satellite remote sensing is used to infer observable surface quantities directly or indirectly, with particular emphasis on key parameters necessary for weather and climate prediction. Coordinated high-resolution remote-sensing and modelling/assimilation capabilities for the Earth surface are required to support an international application-focused effort

NASA CYGNSS Mission Applications Workshop

NASA's Cyclone Global Navigation Satellite System, (CYGNSS), mission is a constellation of eight microsatellites that will measure surface winds in and near the inner cores of hurricanes, including regions beneath the eyewall and intense inner rainbands that could not previously be measured from space. The CYGNSS-measured wind fields, when combined with precipitation fields (e.g., produced by the Global Precipitation Measurement [GPM] core satellite and its constellation of precipitation imagers), will provide coupled observations of moist atmospheric thermodynamics and ocean surface response, enabling new insights into hurricane inner core dynamics and energetics. The outcomes of this workshop, which are detailed in this report, comprise two primary elements: (1) A report of workshop proceedings, and; (2) Detailed Applications Traceability Matrices with requirements and operational considerations to serve broadly for development of value-added tools, applications, and products

Excess path delays from sentinel interferometry to improve weather forecasts

A synthetic aperture radar can offer not only an accurate monitoring of the earth surface deformation, but also information on the troposphere, such as the total path delay or the columnar water vapor at high horizontal resolution. This can be achieved by proper interferometric processing and postprocessing of the radar interferograms. The fine and unprecedented horizontal resolution of the tropospheric products can offer otherwise unattainable information to be assimilated into numerical weather prediction models, which are progressively increasing their resolving capabilities. A number of tricks on the most effective processing approaches, as well as a novel method to pass from multipass differential interferometry products to absolute tropospheric columnar quantities are discussed. The proposed products and methods are assessed using real Sentinel-1 data. The experiment aims at evaluating the accuracy of the derived information and its impact on the weather prediction skill for two meteorological events in Italy. The main perspective of the study is linked to the possibility of exploiting interferometric products from a geosynchronous platform, thus complementing the inherent high resolution of SAR sensors with the required frequent revisit needed for meteorological applications

Space-Based Remote Sensing of the Earth: A Report to the Congress

The commercialization of the LANDSAT Satellites, remote sensing research and development as applied to the Earth and its atmosphere as studied by NASA and NOAA is presented. Major gaps in the knowledge of the Earth and its atmosphere are identified and a series of space based measurement objectives are derived. The near-term space observations programs of the United States and other countries are detailed. The start is presented of the planning process to develop an integrated national program for research and development in Earth remote sensing for the remainder of this century and the many existing and proposed satellite and sensor systems that the program may include are described

Chilean wildfires: probabilistic prediction, emergency response and public communication

The 2016/17 wildfire season in Chile was the worst on record, burning more than 600,000 hectares. Whilst wildfires are an important natural process in some areas of Chile supporting its diverse ecosystems, wildfires are also one of the biggest threats to Chile’s unique biodiversity and it’s timber and wine industries. They also pose a danger to human life and property due to the sharp wildland-urban interface that exists in many Chilean towns and cities. Wildfires are however difficult to predict due to the combination of physical (meteorology, vegetation and fuel condition), and human (population density and awareness level) factors. Most Chilean wildfires are started due to accidental ignition by humans. This accidental ignition could be minimized if an effective wildfire warning system alerted the population to the heightened danger of wildfires in certain locations and meteorological conditions. Here we demonstrate the design of a novel probabilistic wildfire prediction system. The system uses ensemble forecast meteorological data together with a longtime series of fire products derived from Earth Observation to predict not only fire occurrence, but in addition, how intense wildfires could be. The system provides wildfire risk estimation and associated uncertainty for up to 6 days in advance, and communicates it to a variety of end users. The advantage of this probabilistic wildfire warning system over deterministic systems is that it allows users to assess the confidence of a forecast and thus make more informed decisions regarding resource allocation and forest management. The approach used in this study could easily be adapted to communicate other probabilistic forecasts of natural hazards