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Meteorological extremes on the seasonal time scale have received increased attention due to their relevance for society and economy. A recently developed approach to identify seasonal extremes is applied here to ERA5 reanalyses from 1950 to 2020 to identify hot and cold, wet and dry, and stormy and calm extreme seasons globally. The approach consists of (i) fitting a statistical model to seasonal mean values (of temperature, precipitation, and wind speed) at each grid point, (ii) selecting a local return period threshold above which seasonal mean values are deemed extreme, and (iii) forming spatially coherent extreme season objects. The paper introduces the ERA5 extreme season explorer, an open-access web portal enabling researchers to visualize and download extreme season objects of any of the six types in their region of interest, for further investigating their underlying dynamics, statistical properties, and impacts. To illustrate the potential of our extreme season objects, we first discuss the top 10 cold winters in ERA5 globally and then focus on an unusual triple-compound extreme season in winter 1953/54 in Europe, which was simultaneously extremely cold, dry, and calm. We show that detailed analysis of weather system dynamics, including cyclones, blocks, jets, and Rossby waves, provides important insight into the processes leading to extreme seasons. In summary, this study presents for the first time a catalogue of objectively identified extreme seasons in the last decades, shows exemplarily how large-scale dynamics can lead to such seasons, and with the help of the explorer supports the community in accelerating research in this important area at the interface of weather and climate dynamics. [ABSTRACT FROM AUTHOR]

Drought early warning systems (DEWS) have been developed in several countries in response to high socioeconomic losses caused by droughts. In Europe, the European Drought Observatory (EDO) monitors the ongoing drought and forecasts soil moisture anomalies up to 7 days ahead and meteorological drought up to 3 months ahead. However, end users managing water resources often require hydrological drought warning several months in advance. To answer this challenge, a seasonal pan-European DEWS has been developed and has been running in a preoperational mode since mid-2018 under the EU-funded Enhancing Emergency Management and Response to Extreme Weather and Climate Events (ANYWHERE) project. The ANYWHERE DEWS (AD-EWS) is different than other operational DEWS in the sense that the AD-EWS provides a wide range of seasonal hydrometeorological drought forecasting products in addition to meteorological drought, that is, a broad suite of drought indices that covers all water cycle components (drought in precipitation, soil moisture, runoff, discharge, and groundwater). The ability of the AD-EWS to provide seasonal drought predictions in high spatial resolution (5 km × 5 km) and its diverse products mark the AD-EWS as a preoperational drought forecasting system that can serve a broad range of different users' needs in Europe. This paper introduces the AD-EWS and shows some examples of different drought forecasting products, the drought forecast score, and some examples of a user-driven assessment of forecast trust levels. [ABSTRACT FROM AUTHOR]

This paper introduces the new generation of European geostationary meteorological satellites, Meteosat Second Generation (MSG), scheduled for launch in summer 2002. MSG is spin stabilized, as is the current Meteosat series, however, with greatly enhanced capabilities. The 12-channel imager, called the Spinning Enhanced Visible and InfraredImager (SEVIRI), observes the full disk of the earth with an unprecedented repeat cycle of 15 min. SEVIRI has eight channels in the thermal infrared (IR) at 3.9, 6.2, 7.3, 8.7, 9.7, 10.8, 12.0, and 13.4 µm; three channels in the solar spectrum at 0.6, 0.8, and 1.6 µm; and a broadband high-resolution visible channel. The high-resolution visible channel has a spatial resolution of 1.67 km at nadir; pixels are oversampled with a factor of 1.67 corresponding to a samplingdistance of 1 km at nadir. The corresponding values for the eight thermal IR and the other three solar channels are 4.8-km spatial resolution at nadir and an oversampling factor of 1.6, which corresponds to a sampling distanceof 3 km at nadir. Radiometric performance of all channels exceeds specifications. Thermal IR channels have anonboard calibration with an accuracy better than 1 K. Solar channels are calibrated with an operationalvicarious procedure aiming at an accuracy of 5%. Meteorological products are derived in the socalled Satellite Application Facilities (SAF) and in the central Meteorological Product Extraction Facility (MPEF) at the EuropeanOrganisation for the Exploitation of Meteorological Satellites (EUMETSAT) in Darmstadt, Germany. The products supportnowcasting, numerical weather prediction (NWP), and climatological applications. The most important product for NWP, the atmospheric motion vectors, are derived from different channels to improve data coverage and quality. Novelproducts are, among others, indices describing the instability of the clear atmosphere and total column ozone. The paper also discusses the use of MSG for future... [ABSTRACT FROM AUTHOR]

The current state of the art in three-dimensional chemistry-transport models allows them to be considered as mature and reliable enough to be combined with observations networks for implementing integrated air quality monitoring systems over large territories. A cooperative initiative of tour research and operational organizations in France has led to the creation of an integrated air quality platform providing near-real-time and forecasted information using last model developments. Since summer 2003, this system, Prévisions et Observations de la Qualité de l'Air en France et en Europe (PRBV AIR; available online at www.prevair. org), has been delivering daily air quality observations short-term (0-3 days) forecasts, and real-time analyses of ozone, nitrogen dioxide, and particulate matter (PM) at the global and European scales, with a focus over France. It has become a powerful scientific and communication tool to inform the public, sensitive population about the potential occurrence of air pollutant concentrations exceeding regulatory thresholds. The architecture of the operational system is described in this paper. Its setup allows the partners to cope with stringent operational constraints regarding the timely availability of the forecasts and the reliability and the quality of the products delivered. The system relies on a chain of numerical models and methods, and on real-time air quality observations, it uses National Centers for Environmental Predictions (NCEP) and Météo-France weather forecasts. Methods used for producing air pollutant concentration forecasts and analyses are described, as well as the final products, with case examples. A synthesis of the operational system skill results obtained over three consecutive years of operation (2003-06) is given. Lessons learned are presented and issues related to the use of the PREV'AIR system as a complementary tool for air quality management are discussed. [ABSTRACT FROM AUTHOR]

Three algorithm intercomparison experiments have recently been conducted as part of the Global Precipitation Climatology Project with the goal of (a) assessing the skill of current satellite rainfall algorithms, (b) understanding the differences between them, and (c) moving toward improved algorithms. The results of these experiments are summarized and intercompared in this paper. It was found that the skill of satellite rainfall algorithms depends on the regime being analyzed, with algorithms producing very good results in the tropical western Pacific and over Japan and its surrounding waters during summer, but relatively poor rainfall estimates over western Europe during late winter. Monthly rainfall was estimated most accurately by algorithms using geostationary infrared data, but algorithms using polar data [Advanced Very High Resolution Radiometer and Special Sensor Microwave/Imager (SSM/I)] were also able to produce good monthly rainfall estimates when data from two satellites were available. In most eases, SSM/I algorithms showed significantly greater skill than IR-based algorithms in estimating instantaneous rain rates. [ABSTRACT FROM AUTHOR]