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 Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2) reanalysis from January 1980 to June 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.

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.

Rainfall retrieval algorithms often assume a gamma-shaped raindrop size distribution (DSD) with three mathematical parameters N_w, D_m, and μ. If only two independent measurements are available, as with the dual-frequency precipitation radar on the Global Precipitation Measurement (GPM) mission core satellite, then retrieval algorithms are underconstrained and require assumptions about DSD parameters. To reduce the number of free parameters, algorithms can assume that μ is either a constant or a function of D_m. Previous studies have suggested μ–Λ constraints [where Λ = (4 + μ)/D_m], but controversies exist over whether μ–Λ constraints result from physical processes or mathematical artifacts due to high correlations between gamma DSD parameters. This study avoids mathematical artifacts by developing joint probability distribution functions (joint PDFs) of statistically independent DSD attributes derived from the raindrop mass spectrum. These joint PDFs are then mapped into gamma-shaped DSD parameter joint PDFs that can be used in probabilistic rainfall retrieval algorithms as proposed for the GPM satellite program. Surface disdrometer data show a high correlation coefficient between the mass spectrum mean diameter D_m and mass spectrum standard deviation σ_m. To remove correlations between DSD attributes, a normalized mass spectrum standard deviation is constructed to be statistically independent of D_m, with representing the most likely value and std representing its dispersion. Joint PDFs of D_m and μ are created from D_m and . A simple algorithm shows that rain-rate estimates had smaller biases when assuming the DSD breadth of than when assuming a constant μ.

Observations of the vertical structure of rainfall, surface rain rates, and drop size distributions (DSDs) in the southern Appalachians were analyzed with a focus on the diurnal cycle of rainfall. In the inner mountain region, a 5-yr high-elevation rain gauge dataset shows that light rainfall, described here as rainfall intensity less than 3 mm h−1 over a time scale of 5 min, accounts for 30%–50% of annual accumulations. The data also reveal warm-season events characterized by heavy surface rainfall in valleys and along ridgelines inconsistent with radar observations of the vertical structure of precipitation. Next, a stochastic column model of advection–coalescence–breakup of warm rain DSDs was used to investigate three illustrative events. The integrated analysis of observations and model simulations suggests that seeder–feeder interactions (i.e., Bergeron processes) between incoming rainfall systems and local fog and/or low-level clouds with very high number concentrations of small drops (<0.2 mm) govern surface rainfall intensity through driving significant increases in coalescence rates and efficiency. Specifically, the model shows how accelerated growth of small- and moderate-size raindrops (<2 mm) via Bergeron processes can enhance surface rainfall rates by one order of magnitude for durations up to 1 h as in the observations. An examination of the fingerprints of seeder–feeder processes on DSD statistics conducted by tracking the temporal evolution of mass spectrum parameters points to the critical need for improved characterization of hydrometeor microstructure evolution, from mist formation to fog and from drizzle development to rainfall.

Atmospheric rivers (ARs) can cause flooding when they are strong and stall over an already wet watershed. While earlier studies emphasized the role of individual, long-duration ARs in triggering floods, it is not uncommon for floods to be associated with a series of ARs that strike in close succession. This study uses measurements from an atmospheric river observatory at Bodega Bay (BBY), in Northern California, to identify periods when multiple AR events occurred in rapid succession. Here, an AR “event” is the period when AR conditions are present continuously at BBY. An objective method is developed to identify such periods, and the concept of “AR families” is introduced. During the period studied there were 228 AR events. Using the AR family identification method, a range of aggregation periods (the length of time allowed for ARs to be considered part of a family) was tested. For example, for an aggregation period of 5 days, there were 109 AR families, with an average of 2.7 ARs per family. Over a range of possible aggregation periods, typically there were 2–6 ARs per family. Compared to single AR events, the synoptic environment of AR families is characterized by lower geopotential heights throughout the midlatitude North Pacific, an enhanced subtropical high, and a stronger zonal North Pacific jet. Analysis of water year 2017 demonstrated a persistent geopotential height dipole throughout the North Pacific and a positive anomaly of integrated water vapor extending toward California. AR families were favored when synoptic features were semistationary.