The problem of probabilistic forecast verification is approached from a theoretical point of view starting from three basic desiderata: additivity, exclusive dependence on physical observations (“locality”), and strictly proper behavior. By imposing such requirements and only using elementary mathematics, a univocal measure of forecast goodness is demonstrated to exist. This measure is the logarithmic score, based on the relative entropy between the observed occurrence frequencies and the predicted probabilities for the forecast events. Information theory is then used as a guide to choose the scoring-scale offset for obtaining meaningful and fair skill scores. Finally the Brier score is assessed and, for single-event forecasts, its equivalence to the second-order approximation of the logarithmic score is shown. The large part of the presented results are far from being new or original, nevertheless their use still meets with some resistance in the weather forecast community. This paper aims at providing a clear presentation of the main arguments for using the logarithmic score.
Understanding the spatial and temporal variation in carbon fluxes is essential to constrain models that predict climate change. However, our current knowledge of spatial and temporal patterns is uncertain, particularly over land. The ESA GLOBCARBON project aims to generate estimates of at-land products quasi-independent of the original Earth Observation source for use in Dynamic Global Vegetation Models, a central component of the ESSP Global Carbon Project. The service features global estimates of: burned area, f APAR , LAI and vegetation growth cycle. The demonstrator focused on six complete years, from 1998 to 2003 when overlap exists between ESA Earth Observation sensors (ATSR-2, AATSR and MERIS)and VEGETATION but has recently been extended to 2007. This paper presents early results of the first re-processing in the GLOBCARBON project, which was undertaken after comments from users involved in beta testing.
In this paper, we propose a novel Bayesian procedure to update the probability distribution for a set of possible atmospheric states, once ground measures of temperature, pressure, humidity, and tropospheric delay of Global Navigation Satellite System (GNSS) signals are made. It is based on a representative dataset of matching pairs of reanalysis atmospheric states and ground measures. By~applying the basic rules of probability theory and logic inference, a~computable expression for the conditional probability of the states given the measures is found. This allows us to select the most plausible atmospheric conditions, consistent with ground observations. Compared with more conventional techniques, the~proposed approach has the advantage of always giving a result, even if not all the measures are available. Moreover, it provides the probability distributions of the retrieved quantities, which collapse to the corresponding prior distributions in the worst case of no significant measures. In any case, the final uncertainties are fully quantified, as needed for many meteorological applications, including data assimilation and ensemble forecasts for a numerical weather model. In addition to the theoretical details, a practical example of operational application, using a ten-year dataset on a Mediterranean test site, is also presented. The most probable retrieved atmospheric profiles of water vapor and temperature, as well as the corresponding values of precipitable water, are compared with balloon measurements on such a test site, showing good agreement and a significant improvement when the GNSS delay measure is added. In particular, the precipitable water retrieval turns out at least as accurate as that obtained with conventional approaches
Abstract. In the last years coupled atmospheric ocean climate models have remarkably improved medium range seasonal forecasts, especially on middle latitude areas such as Europe and the Mediterranean basin. In this study a new framework for medium range seasonal forecasts is proposed. It is based on circulation types extracted from long range global ensemble models and it aims at two goals: (i) an easier use of the information contained in the complex system of atmospheric circulations, through their reduction to a limited number of circulation types and (ii) the computation of high spatial resolution probabilistic forecasts for temperature and precipitation. The proposed framework could be also useful to lead predictions of weather-derived parameters, such as the risk of heavy rainfall, drought or heat waves, with important impacts on agriculture, water management and severe weather risk assessment. Operatively, starting from the ensemble predictions of mean sea level pressure and geopotential height at 500 hPa of the NCEP – CFSv2 long range forecasts, the third-quantiles probabilistic maps of 2 m temperature and precipitation are computed through a Bayesian approach by using E-OBS 0.25∘ gridded datasets. Two different classification schemes with nine classes were used: (i) Principal Component Transversal (PCT9), computed on mean sea level pressure and (ii) Simulated Annealing Clustering (SAN9), computed on geopotential height at 500 hPa. Both were chosen for their best fit concerning the ground-level precipitation and temperature stratification for the Italian peninsula. Following this approach an operative chain based on a very flexible and exportable method was implemented, applicable wherever spatially and temporally consistent datasets of weather observations are available. In this paper the model operative chain, some output examples and a first attempt of qualitative verification are shown. In particular three case studies (June 2003, February 2012 and July 2014) were examined, assuming that the ensemble seasonal model correctly predicts the circulation type occurrences. At least on this base, the framework here proposed has shown promising performance.
Measurements of spectro-directional radiances done with the imaging spectrometer CHRIS on-board the agile platform PROBA are being used to determine key properties of terrestrial vegetation at the appropriate spatial resolution. These data on vegetation properties can then be used to improve the accuracy and the parameterizations of models describing biosphere processes, i.e. photosynthesis and water use by irrigated crops and trees. The vegetation properties considered are: albedo, Leaf Area Index (LAI), fractional cover, fraction of absorbed photosynthetically active radiation (fAPAR) and canopy chlorophyll content. The Natural Park of San Rossore (Pisa, Central Italy) is a primary test site for several national and international research projects dealing with forest ecosystem monitoring. In particular, since 1999 measurements of transpiration and ecosystem gas-exchange have been regularly taken in the park pine forest to characterize its main water and carbon fluxes. In the same period, several aerial flights have been carried out with onboard hyper-spectral sensors (MIVIS, VIRS, AISA), while a series of satellite images have been acquired using both conventional (NOAA-AVHRR, Landsat-TM/ETM+) and advanced sensors (CHRIS-PROBA). The final objective of these activities is to calibrate and validate methodologies which integrate remotely sensed and ancillary data for monitoring forest ecosystem. More specifically, a major research effort has been focused on evaluating the additional information content provided by advanced hyper-spectral multi-angular sensors about the main parameters needed for forest characterization (species, LAI, pigment content, etc.). These activities are part of projects which are financed by the Italian and European Space Agencies (ASI and ESA, respectively) within the framework of the CHRIS-PROBA and SPECTRA missions. During 2002 and 2003 nine complete multi-angular acquisitions were successfully performed over the San Rossore site. This paper summarizes first results of the evaluation of data acquired so far, particularly forward modeling of Top Of Canopy (TOC) reflectances. The models KUUSK, SAIL and GeoSAIL were used to simulate spectro-directional reflectance of different stands in the forest and compared with PROBA -CHRIS and airborne hyperspectral observations. Deviations of simulated from observed reflectances were significant.
During the night between 9 and 10 September 2017, multiple flash floods associated with a heavy-precipitation event affected the town of Livorno, located in Tuscany, Italy. Accumulated precipitation exceeding 200 mm in two hours was recorded. This rainfall intensity is associated with a return period of higher than 200 years. As a consequence, all the largest streams of the Livorno municipality flooded several areas of the town. We used the limited-area weather research and forecasting (WRF) model, in a convection-permitting setup, to reconstruct the extreme event leading to the flash floods. We evaluated possible forecasting improvements emerging from the assimilation of local ground stations and X- and S-band radar data into the WRF, using the configuration operational at the meteorological center of Tuscany region (LaMMA) at the time of the event. Simulations were verified against weather station observations, through an innovative method aimed at disentangling the positioning and intensity errors of precipitation forecasts. A more accurate description of the low-level flows and a better assessment of the atmospheric water vapor field showed how the assimilation of radar data can improve quantitative precipitation forecasts.
During the night between 9 and 10 September 2017, multiple flash floods associated to a heavy-precipitation event affected the town of Livorno, located in Tuscany, Italy. Accumulated precipitation exceeding 200 mm in two hours, associated with a return period higher than 200 years, caused all the largest streams of the Livorno municipality to flood several areas of the town. We used the limited-area Weather Research and Forecasting (WRF) model, in a convection-permitting setup, to reconstruct the extreme event leading to the flash floods. We evaluated possible forecasting improvements emerging from the assimilation of local ground stations and X- and S-band radar data into the WRF, using the configuration operational at the meteorological center of Tuscany region (LaMMA) at the time of the event. Simulations were verified against weather station observations, through an innovative method aimed at disentangling the positioning and intensity errors of precipitation forecasts. By providing more accurate descriptions of the low-level flow and a better assessment of the atmospheric water vapour, the results demonstrate that assimilating radar data improved the quantitative precipitation forecasts.
Abstract. Scientific institutes contribute to increase scientific awareness in local communities. They also provide students and teachers with learning opportunities outside the classroom. This is especially true when science centers create opportunities to visit laboratories or design activities based on learning by doing. LaMMA, a public consortium set up by Italian National Research Council and Tuscany Region (Italy), is the official weather service for Tuscany. In recent years LaMMA developed several educational modules on meteorology for different school grades. Activities are performed during a two-hour visit at the LaMMA laboratory. Since 2011 every year more than 1200 students come to visit LaMMA to follow one of the proposed modules on meteorology. Students are engaged in different activities and have the opportunity to visit the LaMMA weather operations room and meet the forecasters. In the last two years, an educational module on climate change based on a participatory approach was proposed to teachers of all school levels. More than 500 teachers and environmental educators from all over Tuscany participated and many of them developed a follow up project in the classroom.
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