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Andrew Hoell
,
Forest Cannon
, and
Mathew Barlow

Abstract

The spatial and temporal evolution of Middle East and southwest Asia (MESW) precipitation characteristics and the associated atmospheric circulation during times in which tropical eastern Indian Ocean precipitation is either enhanced or reduced associated with the Madden–Julian oscillation (MJO) is assessed. Using multiple estimates of both the observed precipitation and the MJO during 1981–2016, the evolution of MESW precipitation characteristics throughout November–April is examined in terms of monthly precipitation accumulation on precipitation days, the number of precipitation days, and the number of extreme precipitation days. MJO phases 2–4, during which eastern Indian Ocean precipitation is enhanced, and MJO phases 6–8, during which eastern Indian Ocean precipitation is reduced, are related, with significant decreases and increases in the number of precipitation days across MESW, respectively. The patterns of precipitation-day changes between MJO phases undergo noteworthy spatial and temporal evolutions across the boreal cold season that are influenced by the interaction between Rossby wave forcing by the MJO and seasonal changes in both the upper-level jet and moisture over the region. During December–January, the changes in precipitation days are found primarily over northern MESW, while during February–March, the changes in precipitation days are found primarily over southern MESW. Although the results identify an important sensitivity in the number of precipitation days over the MESW related to the MJO, the same sensitivity is not apparent in terms of the number of extreme precipitation days and, in particular, the amount of precipitation on a precipitation day.

Full access
Emilie Tarouilly
,
Forest Cannon
, and
Dennis P. Lettenmaier

Abstract

We analyze uncertainty in model-based estimates of probable maximum precipitation (PMP) as used in dam spillway design. Our focus is on model-based PMP derived from Weather Research and Forecasting (WRF) Model reconstructions of severe historical storms, amplified by the addition of moisture in the boundary conditions [so-called relative humidity maximization (RHM)]. By scaling moisture and predicting the resulting precipitation, the model-based approach arguably is more realistic than currently used techniques [documented in NOAA’s Hydrometeorological Reports (HMRs)], which assume that precipitation scales linearly with moisture. Despite the important improvement this represents, model-based PMP is subject to several sources of uncertainty that have slowed adoption in operational settings. We analyze an ensemble of PMP simulations that reflect recognized sources of uncertainty including the following: 1) initial condition error, 2) choice of physics parameterizations, and 3) upscale propagating model errors. We apply this ensemble approach to the Feather River watershed (Oroville Dam) in California for the storms of February 1986 and January 1997, which produced some of the largest floods on record at that location, after carrying out in-depth evaluations of model reconstructions. Differences in the maximized 72-h precipitation totals across the 56 ensemble members we produced for each storm are modest, ranging from ±7% of ensemble mean. Our results suggest that while model-based PMP estimates should be interpreted as a range of values, model uncertainty appears to be relatively small for the major atmospheric river–driven flood events we investigated.

Free access
Michael D. Sierks
,
Julie Kalansky
,
Forest Cannon
, and
F. M. Ralph

Abstract

The North American monsoon (NAM) is the main driver of summertime climate variability in the U.S. Southwest. Previous studies of the NAM have primarily focused on the Tier I region of the North American Monsoon Experiment (NAME), spanning central-western Mexico, southern Arizona, and New Mexico. This study, however, presents a climatological characterization of summertime precipitation, defined as July–September (JAS), in the Lake Mead watershed, located in the NAME Tier II region. Spatiotemporal variability of JAS rainfall is examined from 1981 to 2016 using gridded precipitation data and the meteorological mechanisms that account for this variability are investigated using reanalyses. The importance of the number of wet days (24-h rainfall ≥1 mm) and extreme rainfall events (95th percentile of wet days) to the total JAS precipitation is examined and shows extreme events playing a larger role in the west and central basin. An investigation into the dynamical drivers of extreme rainfall events indicates that anticyclonic Rossby wave breaking (RWB) in the midlatitude westerlies over the U.S. West Coast is associated with 89% of precipitation events >10 mm (98th percentile of wet days) over the Lake Mead basin. This is in contrast to the NAME Tier I region where easterly upper-level disturbances such as inverted troughs are the dominant driver of extreme precipitation. Due to the synoptic nature of RWB events, corresponding impacts and hazards extend beyond the Lake Mead watershed are relevant for the greater U.S. Southwest.

Free access
Andrew Hoell
,
Mathew Barlow
,
Forest Cannon
, and
Taiyi Xu

Abstract

While a strong influence on cold season southwest Asia precipitation by Pacific sea surface temperatures (SSTs) has been previously established, the scarcity of southwest Asia precipitation observations prior to 1960 renders the region’s long-term precipitation history largely unknown. Here a large ensemble of atmospheric model simulations forced by observed time-varying boundary conditions for 1901–2012 is used to examine the long-term sensitivity of November–April southwest Asia precipitation to Pacific SSTs. It is first established that the models are able to reproduce the key features of regional variability during the best-observed 1960–2005 period and then the pre-1960 variability is investigated using the model simulations.

During the 1960–2005 period, both the mean precipitation and the two leading modes of precipitation variability during November–April are reasonably simulated by the atmospheric models, which include the previously identified relationships with El Niño–Southern Oscillation (ENSO) and the multidecadal warming of Indo-Pacific SSTs. Over the full 1901–2012 period, there are notable variations in precipitation and in the strength of the SST influence. A long-term drying of the region is associated with the Indo-Pacific warming, with a nearly 10% reduction in westernmost southwest Asia precipitation during 1938–2012. The influence of ENSO on southwest Asia precipitation varied in strength throughout the period: strong prior to the 1950s, weak between 1950 and 1980, and strongest after the 1980s. These variations were not antisymmetric between ENSO phases. El Niño was persistently related with anomalously wet conditions throughout 1901–2012, whereas La Niña was not closely linked to precipitation anomalies prior to the 1970s but has been associated with exceptionally dry conditions thereafter.

Full access
Leila M. V. Carvalho
,
Charles Jones
,
Forest Cannon
, and
Jesse Norris

Abstract

The Indian monsoon system (IMS) is among the most complex and important climatic features on land. This study proposes a simple and robust method to investigate large-scale variations and changes in the IMS that accounts for fluctuations in amplitude, onset, and duration of the summer monsoon, including active and break phases, and the postmonsoon season. This study uses 35 years (1979–2013) of daily data from the National Centers for Environmental Prediction (NCEP) Climate Forecast System Reanalysis (CFSR) at 1° resolution and indicates great potential for application to other reanalyses and climate model datasets. The method is based on combined EOF (CEOF) analysis of variables associated with the IMS’s seasonal cycle (precipitation, circulation at 10 m, and temperature and specific humidity at 2 m). The first CEOF (CEOF-1) explains ~40% of the total variance and represents the continental-scale Asian monsoon. The second CEOF (CEOF-2) explains 11% of the variance and characterizes the Indian monsoon variability, including increased precipitation over western, central, and northern parts of India and the monsoon onset and demise over those regions. Thus, CEOF-2 is referred to as the large-scale index for the Indian monsoon system (LIMS). It is shown that LIMS’s amplitude is strongly correlated with the total June–September precipitation over India. LIMS is continuous in time and can be used to evaluate significant postmonsoon rainfall events that often affect the Indian subcontinent. Moreover, LIMS exhibits spectral variance on intraseasonal time scales that are associated with active and break phases of the monsoon during summer and enhanced rainfall in the postmonsoon period.

Full access
Andrew Hoell
,
Shraddhanand Shukla
,
Mathew Barlow
,
Forest Cannon
,
Colin Kelley
, and
Chris Funk

Abstract

Southwest Asia, defined as the region containing the countries of Afghanistan, Iran, Iraq, and Pakistan, is water scarce and receives nearly 75% of its annual rainfall during the boreal cold season of November–April. The forcing of southwest Asia precipitation has been previously examined for the entire boreal cold season from the perspective of climate variability originating over the Atlantic and tropical Indo-Pacific Oceans. This study examines the intermonthly differences in precipitation variability over southwest Asia and the atmospheric conditions directly responsible in forcing monthly November–April precipitation.

Seasonally averaged November–April precipitation over southwest Asia is significantly correlated with sea surface temperature (SST) patterns consistent with Pacific decadal variability (PDV), El Niño–Southern Oscillation (ENSO), and the long-term change of global SST (LT). In contrast, the precipitation variability during the individual months of November–April is unrelated and is correlated with SST signatures that include PDV, ENSO, and LT in different combinations.

Despite strong intermonthly differences in precipitation variability during November–April over southwest Asia, similar atmospheric circulations, highlighted by a stationary equivalent barotropic Rossby wave centered over Iraq, force the monthly spatial distributions of precipitation. Tropospheric flow on the eastern side of the equivalent barotropic Rossby wave modifies the flux of moisture and advects the mean meridional temperature gradient, resulting in temperature advection that is balanced by vertical motions over southwest Asia. The forcing of monthly southwest Asia precipitation by equivalent barotropic Rossby waves is different from the forcing by baroclinic Rossby waves associated with tropically forced–only modes of climate variability.

Full access
Vesta Afzali Gorooh
,
Eric J. Shearer
,
Phu Nguyen
,
Kuolin Hsu
,
Soroosh Sorooshian
,
Forest Cannon
, and
Marty Ralph

Abstract

Most heavy precipitation events and extreme flooding over the U.S. Pacific coast can be linked to prevalent atmospheric river (AR) conditions. Thus, reliable quantitative precipitation estimation with a rich spatiotemporal resolution is vital for water management and early warning systems of flooding and landslides over these regions. At the same time, high-quality near-real-time measurements of AR precipitation remain challenging due to the complex topographic features of land surface and meteorological conditions of the region: specifically, orographic features occlude radar measurements while infrared-based algorithms face challenges, differentiating between both cold brightband (BB) precipitation and the warmer nonbrightband (NBB) precipitation. It should be noted that the latter precipitation is characterized by greater orographic enhancement. In this study, we evaluate the performance of a recently developed near-real-time satellite precipitation algorithm: Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN) Dynamic Infrared–Rain Rate-Now (PDIR-Now). This model is primarily dependent on infrared information from geostationary satellites as input; consequently, PDIR-Now has the advantage of short data latency, 15–60-min delay between observation to precipitation product delivery. The performance of PDIR-Now is analyzed with a focus on AR-related events for cases dominated by NBB and BB precipitation over the Russian River basin. In our investigations, we utilize S-band (3-GHz) precipitation profilers with Joss/Parsivel disdrometer measurements at the Middletown and Santa Rosa stations to classify BB and NBB precipitation events. In general, our analysis shows that PDIR-Now is more skillful in retrieving precipitation rates over both BB and NBB events across the topologically complex study area as compared to PERSIANN-Cloud Classification System (CCS). Also, we discuss the performance of well-known operational near-real-time precipitation products from 2017 to 2019. Conventional categorical and volumetric categorical indices, as well as continuous statistical metrics, are used to show the differences between various high-resolution precipitation products such as Multi-Radar Multi-Sensor (MRMS).

Open access
Anirudhan Badrinath
,
Luca Delle Monache
,
Negin Hayatbini
,
Will Chapman
,
Forest Cannon
, and
Marty Ralph

Abstract

A machine learning method based on spatial convolution to capture complex spatial precipitation patterns is proposed to identify and reduce biases affecting predictions of a dynamical model. The method is based on a combination of a classification and dual-regression model approach using modified U-Net convolutional neural networks (CNN) to postprocess daily accumulated precipitation over the U.S. West Coast. In this study, we leverage 34 years of high-resolution deterministic Western Weather Research and Forecasting (West-WRF) precipitation reforecasts as training data for the U-Net CNN. The data are split such that the test set contains 4 water years of data that encompass characteristic West Coast precipitation regimes: El Niño, La Niña, and dry and wet El Niño–Southern Oscillation (ENSO neutral) water years. On the unseen 4-yr dataset, the trained CNN yields a 12.9%–15.9% reduction in root-mean-square error (RMSE) and 2.7%–3.4% improvement in Pearson correlation (PC) over West-WRF for lead times of 1–4 days. Compared to an adapted model output statistics correction, the CNN reduces RMSE by 7.4%–8.9% and improves PC by 3.3%–4.2% across all events. Effectively, the CNN adds more than a day of predictive skill when compared to West-WRF. The CNN outperforms the other methods also for the prediction of extreme events, which we define as the top 10% of events with the greatest average daily accumulated precipitation. The improvement over West-WRF’s RMSE (PC) for these events is 19.8%–21.0% (4.9%–5.5%) and MOS’s RMSE (PC) is 8.8%–9.7% (4.2%–4.7%). Hence, the proposed U-Net CNN shows significantly improved forecast skill over existing methods, highlighting a promising path forward for improving precipitation forecasts.

Significance Statement

Extreme precipitation events and atmospheric rivers, which contain narrow bands of water vapor transport, can cause millions of dollars in damages. We demonstrate the utility of a computer vision-based machine learning technique for improving precipitation forecasts. We show that there is a significant increase in predictive accuracy for daily accumulated precipitation using these machine learning methods, over a 4-yr period of unseen cases, including those corresponding to the extreme precipitation associated with atmospheric rivers.

Free access
Alison Cobb
,
Daniel Steinhoff
,
Rachel Weihs
,
Luca Delle Monache
,
Laurel DeHaan
,
David Reynolds
,
Forest Cannon
,
Brian Kawzenuk
,
Caroline Papadopolous
, and
F. M. Ralph

Abstract

This study presents a high-resolution regional reforecast based on the Weather Research and Forecasting (WRF) Model, tailored for the prediction of extreme hydrometeorological events over the western United States (West-WRF) spanning 34 cool seasons (1 December–31 March) from 1986 to 2019. The West-WRF reforecast has a 9-km domain covering western North America and the eastern Pacific Ocean and a 3-km domain covering much of California. The West-WRF reforecast is generated by dynamically downscaling the control member of the Global Ensemble Forecasting System (GEFS) v10 reforecast. Verification of near-surface temperature, wind, and humidity highlight the added value in the reforecast relative to GEFS. Analysis of geopotential height indicates that West-WRF reduces the bias throughout much of the troposphere during early lead times. The West-WRF reforecast also shows clear improvement in atmospheric river characteristics (intensity and landfall) over GEFS. Analysis of mean areal precipitation (MAP) shows that at the basin scale, the reforecast can improve MAP relative to GEFS and reveals a consistent low bias in the reforecast for a coastal watershed (Russian) and a high bias observed in a Northern Sierra watershed (Yuba). The reforecast has a dry bias in seasonal precipitation in the northern Central Valley and Coast Mountain ranges, and a wet bias in the Northern Sierra Nevada, consistent with other operational high-resolution (<25 km) regional models. The applications of this high-resolution multiyear reforecast include process-based studies, assessment of model performance, and machine learning applications.

Restricted access
Joel R. Norris
,
F. Martin Ralph
,
Reuben Demirdjian
,
Forest Cannon
,
Byron Blomquist
,
Christopher W. Fairall
,
J. Ryan Spackman
,
Simone Tanelli
, and
Duane E. Waliser

Abstract

Combined airborne, shipboard, and satellite measurements provide the first observational assessment of all major terms of the vertically integrated water vapor (IWV) budget for a 150 km × 160 km region within the core of a strong atmospheric river over the northeastern Pacific Ocean centered on 1930 UTC 5 February 2015. Column-integrated moisture flux convergence is estimated from eight dropsonde profiles, and surface rain rate is estimated from tail Doppler radar reflectivity measurements. Dynamical convergence of water vapor (2.20 ± 0.12 mm h−1) nearly balances estimated precipitation (2.47 ± 0.41 mm h−1), but surface evaporation (0.0 ± 0.05 mm h−1) is negligible. Advection of drier air into the budget region (−1.50 ± 0.21 mm h−1) causes IWV tendency from the sum of all terms to be negative (−1.66 ± 0.45 mm h−1). An independent estimate of IWV tendency obtained from the difference between IWV measured by dropsonde and retrieved by satellite 3 h earlier is less negative (−0.52 ± 0.24 mm h−1), suggesting the presence of substantial temporal variability that is smoothed out when averaging over several hours. The calculation of budget terms for various combinations of dropsonde subsets indicates the presence of substantial spatial variability at ~50-km scales for precipitation, moisture flux convergence, and IWV tendency that is smoothed out when averaging over the full budget region. Across subregions, surface rain rate is linearly proportional to dynamical convergence of water vapor. These observational results improve our understanding of the thermodynamic and kinematic processes that control IWV in atmospheric rivers and the scales at which they occur.

Open access