Integrated Vapor Transport Research Articles

Extreme Greenland blocking and high‐latitude moisture transport

AbstractBlocked atmospheric flows over Greenland and the North Atlantic Arctic (NAA) can be defined by the appearance of an anomalous ridge, many times off the western margin of continents, that deflects traveling cyclones from their usual storm tracks. Atmospheric blocking often produces a strong equatorward deflection of polar air on the eastern flank of the anticyclone, including severe cold episodes in winter, and severe droughts and heat waves in summer. Recent changes in low‐frequency atmospheric circulation in the NAA have increased sensible heat and moisture advection from the mid‐latitudes into this region. In this study, we explore the frequency and seasonality of extreme Greenland blocking, and we explore the relationship between extreme blocking and moisture transport into and over the region.We quantify atmospheric flow blocking over Greenland using the Greenland Blocking Index, and extreme blocking is defined from 1980 to 2019 at the 90th, 95th, 97th, and 99th percentiles for both summer (June to August) and winter (December to February) seasons. Moisture transport over Greenland was defined by calculating daily integrated vapor transport from the ERA‐Interim reanalysis over the region from 15° to 85°W and 55° to 80°N. The frequency of extreme blocking over Greenland was found to have increased in the most recent two decades (2000–2019) compared to the period 1980–1999. In addition, the probability of above‐average moisture transport occurring on a day with extreme blocking is high in both summer and winter, with the highest probability of high moisture transport during an extreme Greenland Blocking Index day in winter. These findings are unique to this work and suggest future work on the role of moisture transport in developing or sustaining blocks over Greenland.

A Comparison of Northern Hemisphere Atmospheric Rivers Detected by a New Image-Processing Based Method and Magnitude-Thresholding Based Methods

A majority of the existing atmospheric rivers (ARs) detection methods is based on magnitude thresholding on either the integrated water vapor (IWV) or integrated vapor transport (IVT). One disadvantage of such an approach is that the predetermined threshold does not have the flexibility to adjust to the fast changing conditions where ARs are embedded. To address this issue, a new AR detection method is derived from an image-processing algorithm that makes the detection independent of AR magnitude. In this study, we compare the North Pacific and Atlantic ARs tracked by the new detection method and two widely used magnitude thresholding methods in the present day climate. The results show considerable sensitivities of the detected AR number, shape, intensities and their accounted IVT accumulations to different methods. In many aspects, ARs detected by the new method lie between those from the two magnitude thresholding methods, but stand out with a greater number of AR tracks, longer track durations, and stronger AR-related moisture transport in the AR tracks. North Pacific and North Atlantic ARs identified by the new method account for around 100–120 × 10 3 kg/m/s IVT within the AR track regions, about 50 % more than the other two methods. This is primarily due to the fact that the new method captures the strong IVT signals more effectively.

Simulation and Projection of Circulations Associated with Atmospheric Rivers along the North American Northeast Coast

AbstractTorrential rainfall occurring along the North American northeast coast (NANC) in summer and autumn is accompanied by strong atmospheric rivers (ARs), which efficiently transport abundant moisture along a narrow-stretched path associated with a low pressure system. In this study, an autodetection method was used to identify ARs that reached the NANC, based on the 6-hourly data of the ERA-Interim reanalysis conducted by the European Centre for Medium-Range Weather Forecasts, in summer and autumn from 1979 to 2016. Stronger ARs tended to occur in the eastern flank of a cyclonic anomaly that covered the entire North American east coast from Florida to Newfoundland, with a positive precipitation anomaly over the NANC. The cyclonic anomalies and precipitation in autumn were stronger but less frequent than those in summer. Cyclonic anomalies were parts of westward-tilting wavelike circulation perturbations moving into North America from the extratropical North Pacific and moving continuously eastward, reaching the east coast in approximately five days. The Geophysical Fluid Dynamics Laboratory (GFDL) High-Resolution Atmospheric Model (HiRAM), which realistically simulates the occurrence frequency and key characteristics of ARs in current climatic conditions, was used to project the AR activity and corresponding circulations in the future warmer climate under the representative concentration pathway 8.5 scenario. The HiRAM that was driven by sea surface temperature changes projected an overall increase in the occurrence of stronger ARs in both summer and autumn and the precipitation strength in autumn along the NANC by the end of the twenty-first century. This projected enhancement was contributed to by two processes—a smaller contribution was from the weakened basin-scale North Atlantic anticyclone but with higher moisture content, and a larger contribution was from the enhancement in anomalous circulation during AR events with integrated vapor transport exceeding the 75th percentile. These results suggest that the influence of strong ARs on the NANC may increase in the warmer future due to the combination of increased water vapor in the large-scale environment (thermodynamic effect) and enhanced anomalous circulations (dynamic effect). The AR-associated circulations in autumn were also projected to have a stronger tropical connection in the warmer future.

Contribution of Atmospheric Rivers to Annual, Seasonal, and Extreme Precipitation Across British Columbia and Southeastern Alaska

AbstractLying in the frontline of the prevailing midlatitude westerlies, British Columbia and southeastern Alaska (BCSAK) often receive copious amounts of precipitation through atmospheric rivers (ARs). This study quantifies the contribution of ARs to annual, seasonal, and extreme precipitation across BCSAK from 1979 to 2012 using a recently developed high‐resolution gridded precipitation data set, a regional AR catalog, and integrated vapor transport fields calculated from a reanalysis data set. On average, ARs contribute 13% of total annual precipitation with the higher contribution along the coastal regions (up to 33%), parts of which are one of the wettest locations on Earth, followed by the Columbia and Rocky Mountains (~9%–15%). The highest contributions occur during September (up to 57%) and October (up to 49%). The contribution of ARs to extreme precipitation attains >90% along the western arc of the Coast Mountains and the coastal regions of BCSAK. ARs act as the main synoptic‐scale mechanism that brings rainfall to the Rocky Mountains in winter. The probability of observing AR‐related precipitation increases over the study period; however, no change occurs in the average AR‐related precipitation amount for most of BCSAK during 1979–2012. This work provides insights on the critical role ARs play on the water resources of northwestern North America and has broader implications on community water supply and management, hydropower operations, and flood risk assessment and mitigation.

Synchronization and Delay Between Circulation Patterns and High Streamflow Events in Germany

AbstractRiver floods cause extensive losses to economy, ecology, and society throughout the world. They are driven by the space‐time structure of catchment rainfall, which is determined by large‐scale, or even global‐scale, atmospheric processes. The identification of coherent, large‐scale atmospheric circulation structures that determine the moisture transport and convergence associated with rainfall‐induced flooding can help improve its predictability and phenomenology. In this paper, we extend a methodology, used for the analysis of extreme rainfall events, to high streamflow events (HSEs). The approach combines multiple machine learning methods to link HSEs to atmospheric circulation patterns. An application to the German streamflow network using reanalysis data for the period 1960 to 2012 is presented. Daily streamflow from 166 gauges, homogeneously distributed across Germany, are used. Geopotential height fields and integrated vapor transport (IVT) are derived from reanalysis data. An unsupervised neural network, Self Organizing Maps, is applied to geopotential height to identify a finite number of circulation patterns (CPs). Event synchronization between CPs and HSEs is used to establish if they are linked or not. If they are linked, the Event Synchronization method computes the delay between the occurrence of a CP and a HSE. Finally, local logistic regression is used to estimate the probability of occurrence of a HSE, as function of CP and IVT. We demonstrate that our approach is very effective to evaluate HSE probability occurrence across Germany.

Large‐Scale Atmospheric Circulation Patterns Associated With U.S. Great Plains Warm Season Droughts Revealed by Self‐Organizing Maps

AbstractThis study uses a multivariate self‐organizing map approach to diagnose precipitation anomalies over the United States' Great Plains during the warm season (April–August) and the associated anomalous large‐scale atmospheric patterns, as represented by standardized anomalies of 500 hPa geopotential (Z500′), integrated vapor transport (IVT′), and convective inhibition index (CINi′). Circulation patterns favoring dryness identified by the method are generally consistent with those shown in previous studies, but this study provides a more comprehensive and probabilistic characterization of those that favor drought over the Southern Great Plains (SGP) and the Central Great Plains (CGP) and their temporal evolutions. Six circulation types that are associated with warm season rainfall variability over the Great Plains are identified. The SGP droughts are attributable to more frequent and persistent northern low‐southern high as well as dominant high circulation types and are connected to larger negative CINi′. In contrast, CGP droughts are attributable to more frequent and persistent western low‐eastern high, or northern high‐southern low, or dominant high patterns, and are linked to a larger negative IVT′, but not larger CINi′. Thus, these results suggest that land surface dryness and a stable atmospheric boundary layer may play a more important role over the SGP than reduced moisture transport in warm season droughts, but reduced moisture transport may play a more important role than thermodynamic stability in droughts over the CGP.

Simulating and Evaluating Atmospheric River‐Induced Precipitation Extremes Along the U.S. Pacific Coast: Case Studies From 1980–2017

AbstractAtmospheric rivers (ARs) are responsible for a majority of extreme precipitation and flood events along the U.S. West Coast. To better understand the present‐day characteristics of AR‐related precipitation extremes, a selection of nine most intense historical AR events during 1980–2017 is simulated using a dynamical downscaling modeling framework based on the Weather Research and Forecasting Model. We find that the chosen framework and Weather Research and Forecasting Model configuration reproduces both large‐scale atmospheric features—including parent synoptic‐scale cyclones—as well as the filamentary corridors of integrated vapor transport associated with the ARs themselves. The accuracy of simulated extreme precipitation maxima, relative to in situ and interpolated gridded observations, improves notably with increasing model resolution, with improvements as large as 40–60% for fine scale (3 km) relative to coarse‐scale (27 km) simulations. A separate set of simulations using smoothed topography suggests that much of these gains stem from the improved representation of complex terrain. Additionally, using the 12 December 1995 storm in Northern California as an example, we demonstrate that only the highest‐resolution simulations resolve important fine‐scale features—such as localized orographically forced vertical motion and powerful near hurricane‐force boundary layer winds. Given the demonstrated ability of a targeted dynamical downscaling framework to capture both local extreme precipitation and key fine‐scale characteristics of the most intense ARs in the historical record, we argue that such a configuration may be highly conducive to understanding AR‐related extremes and associated changes in a warming climate.

Atmospheric rivers impacting mainland China and Australia: climatology and interannual variations

In this study we have built two atmospheric river (AR) databases for mainland China and Australia using Japanese 55-year Reanalysis data with manual detections. By manually checking the magnitude, shape and orientation of vertically integrated vapour transport fields calculated from the reanalysis data and analysing its embedded synoptic patterns and other meteorological information, we detected 625 AR events over mainland China during 1986–2016 and 576 AR events over the Australian continent during 1977–2016. This manuscript documents the mean climatology, spatial distributions, seasonality and interannual variations of ARs occurring in these two regions. We also assessed possible underlying drivers influencing AR activities. Our results showed that: (i) most ARs over mainland China occured in its lower latitudes, including southern, eastern and central China, but ARs also reached its far north and northeast regions. In Australia, most ARs occurred in the states of Western Australia, South Australia and part of New South Wales and Victoria. These regions of high AR frequencies also frequently experienced Northwest Cloud Bands during the cool season; (ii) ARs in China reached their peak during the East Asian summer monsoon season (May–September). This was also the period when AR frequency in the Australian region tended to be higher, but its seasonal variation was weaker than in China; (iii) ARs exhibited large interannual variations in both regions and a declining trend in central and eastern China; (iv) there was a notable influence of tropical sea surface temperatures (SSTs) on the AR activities in the region, with the ARs in Australia being particularly affected by Indian Ocean SSTs and El-Niño Southern Oscillation (ENSO) in the tropical Pacific. ARs in China appear to be affected by ENSO in its decaying phase, with more ARs likely occurring in boreal summer following a peak El Nino during its preceding winter; (v) the Western Pacific Subtropical High plays a dominant role in forming major moisture transport channels for ARs in China, and South China Sea appears to be a key moisture source. In the Australian region, warm and moist air from the eastern part of the tropical Indian Ocean plays a significant role for ARs in the western part of the continent. In addition, moisture transport from the Coral Sea region was an important moisture source for ARs in its east. Results from this study have demonstrated the value of using AR diagnosis to better understand processes governing climate variations in the A–A region.

Impact of Distinct Origin Locations on the Life Cycles of Landfalling Atmospheric Rivers Over the U.S. West Coast

AbstractAn atmospheric river (AR) event represents strong poleward moisture transport and is defined as a series of spatiotemporally connected instantaneous AR objects. Utilizing an AR tracking algorithm with a depth‐first search (a widely used algorithm in computer science), we examine the life cycle characteristics of AR events that make landfall over the U.S. West Coast by their distinct origin locations. Landfalling AR events from the Northwest Pacific (120°E–170°W, WLAR events) temporally last longer (5.3 versus 3.6 days on average) and have stronger intensity of integrated vapor transport (508 versus 388 kg m−1 s−1 on average) than those originating from the Northeast Pacific (125°W–170°W, ELAR events). A persistent tripole geopotential height anomaly pattern over the North Pacific modulates the origin locations and propagation of landfalling AR events. WLAR events are associated with anomalous highs over northeastern Asia and the Northeast Pacific and an anomalous low over the central North Pacific. This pattern provides favorable conditions for WLAR events to start, propagate northeastward, and make landfall in the northwestern West Coast. WLAR events contribute approximately 25% of the total winter precipitation over Washington and British Columbia. ELAR events are associated with a similar tripole pattern to the WLAR events with an eastward shift. The anomalous low over the Northeast Pacific helps ELAR events to start, propagate northeastward, and make landfall in the southwestern West Coast. Precipitation induced by ELAR events contributes up to 30% of total winter precipitation over California.

Developing an Automated Medium-Range Flood Awareness System for Switzerland Based on Probabilistic Forecasts of Integrated Water Vapor Fluxes

Abstract Floods in the Alpine region can be destructive and cause large economic losses. Many rivers and lakes in Switzerland are regulated and flood damage can be mitigated through an optimal management of lake levels and runoff. This requires high-quality forecasts of atmospheric flood precursors extending beyond short-range (forecast days 1–5) predictions. In several places around the world atmospheric rivers or extreme integrated vapor transport (IVT) are causally related to flood events. Also in Switzerland, extreme IVT oriented perpendicular to the main orography heralds extreme flood events. This relationship is exploited in an operational flood warning system on the medium-range (here forecast days 6–10) time scale based on probabilistic medium-range forecasts of IVT and precipitation over Switzerland provided by the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS). This entails first a comprehensive probabilistic verification of the direction and magnitude of (extreme) IVT and second the development of compact visualizations for the operational use by hydrologists. Based on 20 years of probabilistic reforecasts, we show that both regular and extreme IVT has a better predictability than precipitation and IVT is predictable out to day 8. As the direction of IVT is of central importance for flood risk in Switzerland, we develop a visualization that summarizes probabilistic information on both the direction and magnitude of the IVT together with users of the product. The result is an operational flood warning system based solely on atmospheric flood precursors to extend flood warning information beyond the range of high-resolution deterministic weather forecasts.

Maximizing ENSO as a source of western US hydroclimate predictability

Until recently, the El Niño–Southern Oscillation (ENSO) was considered a reliable source of winter precipitation predictability in the western US, with a historically strong link between extreme El Niño events and extremely wet seasons. However, the 2015–2016 El Niño challenged our understanding of the ENSO-precipitation relationship. California precipitation was near-average during the 2015–2016 El Niño, which was characterized by warm sea surface temperature (SST) anomalies of similar magnitude compared to the extreme 1997–1998 and 1982–1983 El Niño events. We demonstrate that this precipitation response can be explained by El Niño’s spatial pattern, rather than internal atmospheric variability. In addition, observations and large-ensembles of regional and global climate model simulations indicate that extremes in seasonal and daily precipitation during strong El Niño events are better explained using the ENSO Longitude Index (ELI), which captures the diversity of ENSO’s spatial patterns in a single metric, compared to the traditional Niño3.4 index, which measures SST anomalies in a fixed region and therefore fails to capture ENSO diversity. The physically-based ELI better explains western US precipitation variability because it tracks the zonal shifts in tropical Pacific deep convection that drive teleconnections through the response in the extratropical wave-train, integrated vapor transport, and atmospheric rivers. This research provides evidence that ELI improves the value of ENSO as a predictor of California’s seasonal hydroclimate extremes compared to traditional ENSO indices, especially during strong El Niño events.

Improving Atmospheric River Forecasts With Machine Learning

AbstractThis study tests the utility of convolutional neural networks as a postprocessing framework for improving the National Center for Environmental Prediction's Global Forecast System's integrated vapor transport forecast field in the Eastern Pacific and western United States. Integrated vapor transport is the characteristic field of atmospheric rivers, which provide over 65% of yearly precipitation at some western U.S. locations. The method reduces full‐field root‐mean‐square error (RMSE) at forecast leads from 3 hr to seven days (9–17% reduction), while increasing correlation between observations and predictions (0.5–12% increase). This represents an approximately one‐ to two‐day lead time improvement in RMSE. Decomposing RMSE shows that random error and conditional biases are predominantly reduced. Systematic error is reduced up to five‐day forecast lead, but accounts for a smaller portion of RMSE. This work demonstrates convolutional neural networks potential to improve forecast skill out to seven days for precipitation events affecting the western United States.

Rapid Cyclogenesis from a Mesoscale Frontal Wave on an Atmospheric River: Impacts on Forecast Skill and Predictability during Atmospheric River Landfall

Abstract Mesoscale frontal waves have the potential to modify the hydrometeorological impacts of atmospheric rivers (ARs). The small scale and rapid growth of these waves pose significant forecast challenges. We examined a frontal wave that developed a secondary cyclone during the landfall of an extreme AR in Northern California. We document rapid changes in significant storm features including integrated vapor transport and precipitation and connect these to high forecast uncertainty at 1–4-days’ lead time. We also analyze the skill of the Global Ensemble Forecast System in predicting secondary cyclogenesis and relate secondary cyclogenesis prediction skill to forecasts of AR intensity, AR duration, and upslope water vapor flux in the orographic controlling layer. Leveraging a measure of reference accuracy designed for cyclogenesis, we found forecasts were only able to skillfully predict secondary cyclogenesis for lead times less than 36 h. Forecast skill in predicting the large-scale pressure pattern and integrated vapor transport was lost by 96-h lead time. For lead times longer than 36 h, the failure to predict secondary cyclogenesis led to significant uncertainty in forecast AR intensity and to long bias in AR forecast duration. Failure to forecast a warm front associated with the secondary cyclone at lead times less than 36 h caused large overprediction of upslope water vapor flux, an important indicator of orographic precipitation forcing. This study highlights the need to identify offshore mesoscale frontal waves in real time and to characterize the forecast uncertainty inherent in these events when creating hydrometeorological forecasts.

Revisiting Moisture Transport During the Indian Summer Monsoon Using the Moisture River Concept

The moisture river (MR) concept is used here to revisit the characteristics of moisture transport during the Indian Summer Monsoon (ISM) season from June to September. The study is carried out using Modern-Era Retrospective analysis for Research and Applications (MERRA) data products for the 30-year period from 1982 to 2011. MRs are detected from vertically integrated vapor transport (IVT) computed using validated MERRA mixing ratios and horizontal wind speeds at vertical levels from the surface to 300 hPa. The characteristics of MR and moisture transport associated with major rainfall episodes of the 2011 ISM are studied in detail. The large moisture content, together with high wind speeds associated with the monsoon low-level jet, contributes to the observed structure of the MR. The outflux of moisture from the Arabian Sea is on average 40% larger than the cross-equatorial flux from the southern Indian Ocean, indicating increased contribution from the Arabian Sea. The upward movement of moisture to the layers above the jet is a decisive factor in the strengthening of the river and major rainfall episodes. Back-trajectory analyses reveal that the path of moisture transport from the southern Indian Ocean is confined to the lower levels, whereas higher-level trajectories originate from the Red Sea, Arabian Sea and southern Indian Ocean. The MR statistics show significant intra-seasonal and inter-annual variability in occurrence and strength, with a near-linear relationship between the IVT strength and the corresponding daily ISM rainfall, especially over the west coast. Wavelet cross-spectral analyses carried out using the daily Arabian Sea IVT maximum and rainfall over the west coast and central India during a deficit and surplus monsoon year depict significant differences in the dominant periodicities, associated with intra-seasonal and inter-annual monsoon variations.

Potential Predictability of Regional Precipitation and Discharge Extremes Using Synoptic-Scale Climate Information via Machine Learning: An Evaluation for the Eastern Continental United States

Abstract Current generation general circulation models (GCMs) simulate synoptic-scale climate state variables such as geopotential heights, specific humidity, and integrated vapor transport (IVT) more reliably than mesoscale precipitation. Statistical downscaling methods that condition precipitation on GCM-based, synoptic-scale climate features have shown promise in the reproduction of local precipitation. However, current approaches to climate-state-informed downscaling impose some limitations on the skill of precipitation reproduction, including hard clustering of climate modes into a discrete set of states, utilization of numerical clustering methodologies poorly suited to nonnormal data, and a tendency to focus on relationships to a limited set of large-scale climate modes. This study presents a methodology based on emerging machine learning techniques to develop global approximators of regional precipitation and discharge extremes given a suite of synoptic-scale climate state variables. Archetypal analysis is first used to define regional modes of winter and summer extreme precipitation and discharge across the eastern contiguous United States. A 2D convolution neural network (NN) is then used to predict the co-occurrence of the archetypes using 300- and 700-hPa geopotential heights, 300- and 700-hPa specific humidity, and IVT. Results suggest that 300-hPa geopotential height, 700-hPa specific humidity, and IVT yield the most reliable predictions, although with some important differences by season and region. Finally, we demonstrate that the trained activations of NN convolutional layers can be used to infer the causal pathways between synoptic-scale climate features and regional extremes.

The Climatological Impact of Recurving North Atlantic Tropical Cyclones on Downstream Extreme Precipitation Events

Abstract This study provides the first climatological assessment of the impact of recurving North Atlantic tropical cyclones (TCs) on downstream precipitation extremes. The response is evaluated based on time-lagged composites for 146 recurving TCs between 1979 and 2013 and quantified by the area affected by precipitation extremes (PEA) in a domain shifted relative to the TC–jet interaction location, which often encompasses major parts of Europe. The statistical significance of the PEA response to the TCs is determined using a novel bootstrapping technique based on flow analogs. A statistically significant increase in PEA is found between lags +42 and +90 h after the TC–jet interaction, with a doubling of the PEA compared to analog cases without recurving TCs. A K-means clustering applied to the natural logarithm of potential vorticity fields [ln(PV)] around the TC–jet interaction points reveals four main flow configurations of North Atlantic TC–jet interactions. Two main mechanisms by which recurving TCs can foster precipitation extremes farther downstream emerge: 1) an “atmospheric river–like” mechanism, with anomalously high integrated vapor transport (IVT) downstream of the recurving TCs and 2) a “downstream-development” mechanism, with anomalously high IVT ahead of a downstream trough. Hereby, the analog bootstrapping technique separates the impact of the TC from that of the midlatitude flow’s natural evolution on the PEA formation. This analysis reveals an unequivocal effect of the TCs for the atmospheric river–like cases, while for the downstream-development cases, a substantial increase in PEA is also found in the analogs without a TC.

Large-Scale Flow Patterns Associated with Extreme Precipitation and Atmospheric Rivers over Norway

AbstractA climatology of extreme cold season precipitation events in Norway from 1979 to 2014 is presented, based on the 99th percentile of the 24-h accumulated precipitation. Three regions, termed north, west, and south are identified, each exhibiting a unique seasonal distribution. There is a proclivity for events to occur during the positive phase of the NAO. The result is statistically significant at the 95th percentile for the north and west regions. An overarching hypothesis of this work is that anomalous moisture flux, or so-called atmospheric rivers (ARs), are integral to extreme precipitation events during the Norwegian cold season. An objective analysis of the integrated vapor transport illustrates that more than 85% of the events are associated with ARs. An empirical orthogonal function and fuzzy cluster technique is used to identify the large-scale weather patterns conducive to the moisture flux and extreme precipitation. Five days before the event and for each of the three regions, two patterns are found. The first represents an intense, southward-shifted jet with a southwest–northeast orientation. The second identifies a weak, northward-shifted, zonal jet. As the event approaches, regional differences become more apparent. The distinctive flow pattern conducive to orographically enhanced precipitation emerges in the two clusters for each region. For the north and west regions, this entails primarily zonal flow impinging upon the south–north-orientated topography, the difference being the latitude of the strong flow. In contrast, the south region exhibits a significant southerly component to the flow.

Numerical reconstruction of the intense precipitation and moisture transport fields for six tropical cyclones affecting the eastern United States

Tropical cyclones (TCs) are intense atmospheric vortices that form over the warm tropical oceans. They are recognized for their ability to generate intense precipitation that may in turn create disastrous floods. This article first assesses the suitability of a regional atmospheric model, the Weather Research and Forecasting (WRF) model, to simulate the intense precipitation depth (PD) fields of six North Atlantic TCs that affected the eastern United States during 2002–2016. Due to the strong nonlinearity involved in tropical cyclones' dynamics and thermodynamics, which causes tropical cyclones' tracks to be very sensitive to the different modeling choices, placing the PD fields in the observed locations was challenging. This involved trying several simulation start dates and combinations of the WRF model's parameterization schemes for each storm simulated. Model performance was evaluated by comparing the simulated PD fields with the observed PD fields obtained from the NCEP Stage IV precipitation dataset. In addition to qualitative comparisons, three quantitative metrics were used to quantify the WRF model performance in simulating a PD field's location, structure and intensity. The sensitivity of the simulation results to the choice of the parameterization schemes was then illustrated using Hurricane Gustav (2008). Eventually, the most satisfactory simulations were used to investigate the mechanisms responsible for the generation of intense precipitation in these TCs. More specifically, the vertically integrated vapor transport field and its divergence were calculated using the model outputs, and it was found that horizontal moisture convergence played a central role in the generation of intense precipitation in these TCs.

Hourly Analyses of the Large Storms and Atmospheric Rivers that Provide Most of California’s Precipitation in Only 10 to 100 Hours per Year

California is regularly impacted by floods and droughts, primarily as a result of too many or too few atmospheric rivers (ARs). This study analyzes a two-decade-long hourly precipitation dataset from 176 California weather stations and a 3-hourly AR chronology to report variations in rainfall events across California and their association with ARs. On average, 10-40 and 60-120 hours of rainfall in southern and northern California, respectively, are responsible for more than half of annual rainfall accumulations. Approximately 10-30% of annual precipitation at locations across the state is from only one large storm. On average, northern California receives 25-45 rainfall events annually (40-50% of which are AR-related). These events typically have longer durations and higher event-precipitation totals than those in southern California. Northern California also receives more AR landfalls with longer durations and stronger Integrated Vapor Transport (IVT). On average, ARs contribute 79%, 76%, and 68% of extreme-rainfall accumulations (i.e., top 5% events annually) in the north coast, northern Sierra, and Transverse Ranges of southern California, respectively. The San Francisco Bay Area terrain gap in the California Coast Range allows more AR water vapor to reach inland over the Delta and Sacramento Valley, and thus, influences precipitation in the Delta’s catchment. This is particularly important for extreme precipitation in the northern Sierra Nevada, including river basins above Oroville Dam and Shasta Dam. This study highlights differences between rainfall and AR characteristics in coastal versus inland northern California, differences that largely determine the regional geography of flood risks and water-reliability. These analyses support water resource, flood, levee, wetland, and ecosystem management within the catchment of the San Francisco estuary system by describing regional characteristics of ARs and their influence on rainfall on an hourly timescale.

Atmospheric rivers impacting Northern California and their modulation by a variable climate

Understanding the role of climate variability in modulating the behavior of land-falling atmospheric rivers (ARs) is important for seasonal and subseasonal predictability for water resource management and flood control. We examine daily activity of ARs targeting the Northern California coast over six decades using observations of synoptic-scale circulation, high-resolution precipitation, and a long-term AR detection catalog to quantify distinct types of land-falling ARs categorized by their circulation features. We demonstrate how dramatically different atmospheric states evolve into landfalling ARs along distinct pathways that are modulated by interannual (El Niño/Southern Oscillation (ENSO) and the Pacific Decadal Oscillation) and subseasonal (Arctic Oscillation, Pacific North American Pattern, Western Pacific Oscillation, and the Eastern Pacific Oscillation) modes of large-scale climate variability. Different configurations of climate variability modes are shown to favor ARs having different characteristics in terms of synoptic evolution, integrated vapor transport and landfall orientation resulting in different patterns of precipitation over the landscape. In particular, our results show that while ENSO plays an important role in modulating the synoptic evolution of ARs and their orientation at landfall, subseasonal regional climate modes, which also influence landfall orientation as well as the position of the storm track, appear to be more influential than ENSO in modulating precipitation variability in California. This could have implications for seasonal to subseasonal (S2S) forecasting. Finally, we examine AR activity over the most recent and highly anomalous winter 2016–2017 and show how the unprecedented wet conditions in Northern California were at least partly due to the persistence of ARs characterized by a southward storm track and southerly orientation, which represent the type of ARs associated with heavy rainfall in California, and which are associated with the negative phase of subseasonal regional teleconnection patterns.