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 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.

10The 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 15 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 20 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 25 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.Geosci. Model Dev. Discuss., https://doi

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Rainfall retrieval algorithms often assume a gamma-shaped raindrop size distribution (DSD) with three mathematical parameters N w , D m , and m. If only two independent measurements are available, as with the dualfrequency 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 m is either a constant or a function of D m . Previous studies have suggested m-L constraints [where L 5 (4 1 m)/D m ], but controversies exist over whether m-L 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 s m . To remove correlations between DSD attributes, a normalized mass spectrum standard deviation s 0 m is constructed to be statistically independent of D m , with s 0 m representing the most likely value and std(s 0 m ) representing its dispersion. Joint PDFs of D m and m are created from D m and s 0 m . A simple algorithm shows that rain-rate estimates had smaller biases when assuming the DSD breadth of s 0 m than when assuming a constant m.

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.

Abstract.A diagnostic analysis of the space-time structure of error in quantitative precipitation estimates (QPEs) from the precipitation radar (PR) on the Tropical Rainfall Measurement Mission (TRMM) satellite is presented here in preparation for the Integrated Precipitation and Hydrology Experiment (IPHEx) in 2014. IPHEx is the first NASA ground-validation field campaign after the launch of the Global Precipitation Measurement (GPM) satellite. In anticipation of GPM, a science-grade high-density raingauge network was deployed at mid to high elevations in the southern Appalachian Mountains, USA, since 2007. This network allows for direct comparison between ground-based measurements from raingauges and satellite-based QPE (specifically, PR 2A25 Version 7 using 5 years of data [2008][2009][2010][2011][2012][2013]. Case studies were conducted to characterize the vertical profiles of reflectivity and rain rate retrievals associated with large discrepancies with respect to ground measurements. The spatial and temporal distribution of detection errors (false alarm, FA; missed detection, MD) and magnitude errors (underestimation, UND; overestimation, OVR) for stratiform and convective precipitation are examined in detail toward elucidating the physical basis of retrieval error.The diagnostic error analysis reveals that detection errors are linked to persistent stratiform light rainfall in the southern Appalachians, which explains the high occurrence of FAs throughout the year, as well as the diurnal MD maximum at midday in the cold season (fall and winter) and especially in the inner region. Although UND dominates the error budget, underestimation of heavy rainfall conditions accounts for less than 20 % of the total, consistent with regional hydrometeorology. The 2A25 V7 product underestimates lowlevel orographic enhancement of rainfall associated with fog, cap clouds and cloud to cloud feeder-seeder interactions over ridges, and overestimates light rainfall in the valleys by large amounts, though this behavior is strongly conditioned by the coarse spatial resolution (5 km) of the topography mask used to remove ground-clutter effects. Precipitation associated with small-scale systems (< 25 km 2 ) and isolated deep convection tends to be underestimated, which we attribute to non-uniform beam-filling effects due to spatial averaging of reflectivity at the PR resolution. Mixed precipitation events (i.e., cold fronts and snow showers) fall into OVR or FA categories, but these are also the types of events for which observations from standard ground-based raingauge networks are more likely subject to measurement uncertainty, that is raingauge underestimation errors due to undercatch and precipitation phase.Overall, the space-time structure of the errors shows strong links among precipitation, envelope orography, landform (ridge-valley contrasts), and a local hydrometeorological regime that is strongly modulated by the diurnal cycle, pointing to three major error causes that are inter-related: (1) representation of concurren...