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Atmospheric rivers (ARs) are now widely known for their association with high-impact weather events and long-term water supply in many regions. Researchers within the scientific community have developed numerous methods to identify and track of ARs—a necessary step for analyses on gridded data sets, and objective attribution of impacts to ARs. These different methods have been developed to answer specific research questions and hence use different criteria (e.g., geometry, threshold values of key variables, and time dependence). Furthermore, these methods are often employed using different reanalysis data sets, time periods, and regions of interest. The goal of the Atmospheric River Tracking Method Intercomparison Project (ARTMIP) is to understand and quantify uncertainties in AR science that arise due to differences in these methods. This paper presents results for key AR-related metrics based on 20+ different AR identification and tracking methods applied to Modern-Era Retrospective Analysis for Research and Applications Version 2 reanalysis data from January 1980 through June 2017. We show that AR frequency, duration, and seasonality exhibit a wide range of results, while the meridional distribution of these metrics along selected coastal (but not interior) transects are quite similar across methods. Furthermore, methods are grouped into criteria-based clusters, within which the range of results is reduced. AR case studies and an evaluation of individual method deviation from an all-method mean highlight advantages/disadvantages of certain approaches. For example, methods with less (more) restrictive criteria identify more (less) ARs and AR-related impacts. Finally, this paper concludes with a discussion and recommendations for those conducting AR-related research to consider.

Atmospheric rivers (ARs) play a vital role in shaping the hydroclimate of many regions globally, and can substantially impact water resource management, emergency response planning, and other socioeconomic entities. The second International Atmospheric Rivers Conference took place at the Scripps Institution of Oceanography, University of California, San Diego, during 25–28 June, 2018, in La Jolla, California, USA. It was sponsored by the Center for Western Weather and Water Extremes (CW3E). A total of 120 people attended the Conference with 94 abstracts submitted and 30 participating students. In addition to the conference, the Student Forecasting Workshop was organised in the same week. During this workshop, students were exposed to AR forecasting tools, and learned examples of how these tools could be used to make decisions for various applications. The main goals of this conference were to bring together experts from across the fields of hydrology, atmospheric, oceanic, and polar sciences, as well as water management, civil engineering, and ecology to advance the state of AR science and to explore the future directions for the field. The conference was organised into traditional oral and poster presentations, along with panel discussions and Breakout Groups. This format allowed enhanced interaction between participants, driving progress within the scientific community and the enhanced communication of societal needs by various stakeholders. Several emerging topics of research were highlighted, including subseasonal-to-seasonal (S2S) prediction of ARs and an overview of the AR Reconnaissance campaign. In addition to providing a forum to disseminate and debate new results from scientific talks and posters, the conference was equally effective and useful in linking scientists to users and decision-makers that require improved knowledge on ARs to manage resources and prepare for hazards. The third International Atmospheric Rivers Conference will be held in Chile in 2020, and hosted by the University of Chile, Santiago.

Windstorms affecting Europe are among the natural hazards with the largest socio-economic impacts. Therefore, many sectors like society, the economy, or the insurance industry are highly interested in reliable information on associated impacts and losses. In this study, we compare – for the first time – estimated windstorm losses using a simplified meteorological loss index (LI) with losses obtained from a complex insurance loss (catastrophe) model, namely the European Windstorm Model of Aon Impact Forecasting. To test the sensitivity of LI to different meteorological input data, we furthermore contrast LI based on the reanalysis dataset ERA5 and its predecessor ERA-Interim. We focus on similarities and differences between the datasets in terms of loss values and storm rank for specific historical storm events in the common reanalysis period across 11 European countries. Our results reveal higher LI values for ERA5 than for ERA-Interim for all of Europe (by roughly a factor of 10), coming mostly from the higher spatial resolution in ERA5. The storm ranking is comparable for western and central European countries for both reanalyses, confirmed by high correlation values between 0.6 and 0.89. Compared to the Aon Impact Forecasting model, LI ERA5 shows comparable storm ranks, with correlation values ranging between 0.45 and 0.8. In terms of normalized loss, LI exhibits overall lower values and smaller regional differences. Compared to the market perspective represented by the insurance loss model, LI seems to have particular difficulty in distinguishing between high-impact events at the tail of the wind gust distribution and moderate-impact events. Thus, the loss distribution in LI is likely not steep enough, and the tail is probably underestimated. Nevertheless, it is an effective index that is suitable for estimating the impacts of storm events and ranking storm events, precisely because of its simplicity. [ABSTRACT FROM AUTHOR]

Southern Africa has been strongly affected by ongoing climate change in recent decades. Rainfall variability is modulated by regional patterns of moisture advection and convergence in the lower troposphere. Using reanalysis, ground and satellite‐based rainfall products and observations from 10 weather stations, we perform a synoptic and climatological analysis focussing on atmospheric circulation, moisture transport and their relationship with rainfall anomalies over the Angolan and Namibian Plateau region. Results clearly show that a stronger (weaker) Zambezi low level jet (LLJ) magnitudes are associated with above (below) normal rainfalls over the main Angolan and Namibian plateaus. With lower confidence, a stronger Limpopo LLJ may also lead to enhanced rainfall over Namibia and southeast Angola. The Zambezi LLJ moisture fluxes are moderately controlled by Mozambique Chanel Trough and Angola Low intensities, while the Limpopo LLJ intensities have very low influence from the Mozambique Chanel Trough and Angola Low, respectively. The Angola Low in its tropical phase is associated with deeper moisture convergence and stronger vertical velocities, leading to higher amounts of precipitable water within the air column, thus enhancing precipitation over the region. It is shown that the major moisture source of rainfall, which is advected to via the Zambezi LLJ, is the Indian Ocean. Meanwhile, the Atlantic Ocean plays a minor role. Given the current lack of observations and projected climate change, further research and investments are urgently needed in the region, for example, regarding the expansion of the surface data network. [ABSTRACT FROM AUTHOR]

Abstract. The Atmospheric River Tracking Method Intercomparison Project (ARTMIP) is an international collaborative effort to understand and quantify the uncertainties in atmospheric river (AR) science based on detection algorithm alone. Currently, there are many AR identification and tracking algorithms in the literature with a wide range of techniques and conclusions. ARTMIP strives to provide the community with information on different methodologies and provide guidance on the most appropriate algorithm for a given science question or region of interest. All ARTMIP participants will implement their detection algorithms on a specified common dataset for a defined period of time. The project is divided into two phases: Tier 1 will utilize the MERRA-2 reanalysis from January 1980 to June of 2017 and will be used as a baseline for all subsequent comparisons. Participation in Tier 1 is required. Tier 2 will be optional and include sensitivity studies designed around specific science questions, such as reanalysis uncertainty and climate change. High resolution reanalysis and/or model output will be used wherever possible. Proposed metrics include AR frequency, duration, intensity, and precipitation attributable to ARs. Here we present the ARTMIP experimental design, timeline, project requirements, and a brief description of the variety of methodologies in the current literature. We also present results from our 1-month proof of concept trial run designed to illustrate the utility and feasibility of the ARTMIP project.