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Clément Guilloteau
and
Efi Foufoula-Georgiou

Abstract

Observations of clouds and precipitation in the microwave domain from the active dual-frequency precipitation radar (DPR) and the passive Global Precipitation Measurement (GPM) Microwave Imager (GMI) onboard the GPM Core Observatory satellite are used in synergy with cloud tracking information derived from infrared imagery from the GOES-13 and Meteosat-7 geostationary satellites for analysis of the life cycle of precipitating cloud systems, in terms of temporal evolution of their macrophysical characteristics, in several oceanic and continental regions of the tropics. The life cycle of each one of the several hundred thousand cloud systems tracked during the 2-yr (2015–16) analysis period is divided into five equal-duration stages between initiation and dissipation. The average cloud size, precipitation intensity, precipitation top height, and convective and stratiform precipitating fractions are documented at each stage of the life cycle for different cloud categories (based upon lifetime duration). The average life cycle dynamics is found remarkably homogeneous across the different regions and is consistent with previous studies: systems peak in size around midlife; precipitation intensity and convective fraction tend to decrease continuously from the initiation stage to the dissipation. Over the three continental regions, Amazonia (AMZ), central Africa (CAF), and Sahel (SAH), at the early stages of clouds’ life cycle, precipitation estimates from the passive GMI instrument are systematically found to be 15%–40% lower than active radar estimates. By highlighting stage-dependent biases in state-of-the-art passive microwave precipitation estimates over land, we demonstrate the potential usefulness of cloud tracking information for improving retrievals and suggest new directions for the synergistic use of geostationary and low-Earth-orbiting satellite observations.

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Savannah K. Jorgensen
and
John W. Nielsen-Gammon

Abstract

This study estimates extreme rainfall trends across the Gulf Coast and southeastern coast of the United States while applying methods for extending the temporal record and aggregating across spatial trend variations. Nonstationary generalized extreme value (GEV) models are applied to historical annual daily maximum precipitation data (1890–2019) while using CMIP5 global mean surface temperature (GMST) as the covariate. County composites and multicounty regions are used for local data record extension and pooling. Unlike most previous studies, return periods as long as 100 years are analyzed. The local trend estimates themselves are found to be too noisy to be reliable as estimates of climate-driven trends. However, application of a Gaussian process model to the spatial distribution of observed trends yields overall trend detection at the 95% significance level. The overall historical increase due to nonstationarity across the study region, with associated 95% confidence intervals, is 9% (3%, 15%) for the 2-yr return period and 16% (4%, 26%) for the 100-yr return period. A trend is also detectable in the Gulf Coast subregion, but not in the smaller southeast subregion. Recent weather events and nonstationarity have caused the official return value estimates for parts of North and South Carolina to be much lower than the return values estimated here.

Significance Statement

Protection of people and infrastructure from flooding relies on accurate estimates of potential extreme rainfall intensity. Some official estimates of extreme rainfall near the Gulf Coast and southeastern coast of the United States are over 20 years old. We show that, across this region, there is a clear trend in daily rainfall so extreme that it only has a 1% chance of happening in any given year (the so-called 100-yr rainfall). This trend means that many existing estimates of extreme rainfall are too low, both now and in the future, so flooding risks based on those estimates would be underestimated as well.

Open access
J. R. Levey
and
A. Sankarasubramanian

Abstract

Precipitation forecasts, particularly at subseasonal-to-seasonal (S2S) time scale, are essential for informed and proactive water resource management. Although S2S precipitation forecasts have been evaluated, no systematic decomposition of the skill, Nash–Sutcliffe efficiency (NSE) coefficient, has been analyzed toward understanding the forecast accuracy. We decompose the NSE of S2S precipitation forecast into its three components—correlation, conditional bias, and unconditional bias—by four seasons, three lead times (1–12, 1–22, and 1–32 days), and three models, European Centre of Medium-Range Weather Forecasts (ECMWF), National Centers for Environmental Prediction’s (NCEP) Climate Forecast System (CFS) model, and Environment and Climate Change Canada (ECCC), over the conterminous United States (CONUS). Application of a dry threshold, removal of grid cells with seasonal climatological precipitation means below 0.01 in. per day, is important as the NSE and correlations are lower across all seasons after masking areas with low precipitation values. Further, a west-to-east gradient in S2S forecast skill exists, and forecast skill was better during the winter months and for areas closer to the coast. Overall, ECMWF’s model performance was stronger than both ECCC and NCEP CFS’s performance, mainly for the forecasts issued during the fall and winter months. However, ECCC and NCEP CFS performed better for the forecast issued during the spring months and for areas further from the coast. Postprocessing using simple model output statistics could reduce both unconditional and conditional biases to zero, thereby offering better skill for regimes with high correlation. Our decomposition results show that efforts should focus on improving model parameterization and initialization schemes for climate regimes with low correlation.

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Yanjuan Wu
,
Ivan D. Haigh
,
Chao Gao
,
Luke J. Jenkins
,
Joshua Green
,
Robert Jane
,
Yu Xu
,
Hengzhi Hu
, and
Naicheng Wu

Abstract

In coastal regions, compound flooding, driven by multiple flood hazard sources, can cause greater damage than when the flood drivers occur in isolation. This study focuses on compound flooding from extreme precipitation and storm surge in China’s Qiantang Estuary. We quantify the potential of compound flooding by measuring bivariate joint statistical dependence and joint return period (JRP). We find a significant positive dependence between the two flood drivers considered, as indicated by Kendall’s rank correlation coefficients. Compound events occur frequently, with an average of 2.65 events per year from 1979 to 2018, highlighting the significant concern of compound flooding for this estuary. Using a copula model, we demonstrate that considering the dependence between the two flood drivers shortens the JRP of compound flooding compared to the JRP assuming total independence. For a 1-in-10-yr precipitation event and 1-in-10-yr storm surge event, the JRP is 1 in 100 years when assuming total independence. However, it decreases to 1 in 32.44 years when considering their dependence. Ignoring the dependence between flood drivers can lead to an increase in the JRP of compound events, resulting in an underestimation of the overall flood risk. Our analysis reveals a strong link between the weather patterns creating compound events and extreme storm surge only events with tropical cyclone activity. Additionally, the extreme precipitation only events were found to be connected with the frontal system of the East Asian summer monsoon. This study highlights the importance of considering the dependence between multiple flood drivers associated with certain types of the same weather systems when assessing the flood risk in coastal regions.

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G. Cristina Recalde-Coronel
,
Benjamin Zaitchik
,
William K. Pan
,
Yifan Zhou
, and
Hamada Badr

Abstract

Hydrological predictions at subseasonal-to-seasonal (S2S) time scales can support improved decision-making in climate-dependent sectors like agriculture and hydropower. Here, we present an S2S hydrological forecasting system (S2S-HFS) for western tropical South America (WTSA). The system uses the global NASA Goddard Earth Observing System S2S meteorological forecast system (GEOS-S2S) in combination with the generalized analog regression downscaling algorithm and the NASA Land Information System (LIS). In this implementation study, we evaluate system performance for 3-month hydrological forecasts for the austral autumn season (March–May) using ensemble hindcasts for 2002–17. Results indicate that the S2S-HFS generally offers skill in predictions of monthly precipitation up to 1-month lead, evapotranspiration up to 2 months lead, and soil moisture content up to 3 months lead. Ecoregions with better hindcast performance are located either in the coastal lowlands or in the Amazon lowland forest. We perform dedicated analysis to understand how two important teleconnections affecting the region are represented in the S2S-HFS: El Niño–Southern Oscillation (ENSO) and the Antarctic Oscillation (AAO). We find that forecast skill for all variables at 1-month lead is enhanced during the positive phase of ENSO and the negative phase of AAO. Overall, this study indicates that there is meaningful skill in the S2S-HFS for many ecoregions in WTSA, particularly for long memory variables such as soil moisture. The skill of the precipitation forecast, however, decays rapidly after forecast initialization, a phenomenon that is consistent with S2S meteorological forecasts over much of the world.

Open access
Habiba Kallel
,
Antoine Thiboult
,
Murray D. Mackay
,
Daniel F. Nadeau
, and
François Anctil

Abstract

Accurately modeling the interactions between inland water bodies and the atmosphere in meteorological and climate models is crucial, given the marked differences with surrounding landmasses. Modeling surface heat fluxes remains a challenge because direct observations available for validation are rare, especially at high latitudes. This study presents a detailed evaluation of the Canadian Small Lake Model (CSLM), a one-dimensional mixed-layer dynamic lake model, in reproducing the surface energy budget and the thermal stratification of a subarctic reservoir in eastern Canada. The analysis is supported by multiyear direct observations of turbulent heat fluxes collected on and around the 85-km2 Romaine-2 hydropower reservoir (50.7°N, 63.2°W) by two flux towers: one operating year-round on the shore and one on a raft during ice-free conditions. The CSLM, which simulates the thermal regime of the water body including ice formation and snow physics, is run in offline mode and forced by local weather observations from 25 June 2018 to 8 June 2021. Comparisons between observations and simulations confirm that CSLM can reasonably reproduce the turbulent heat fluxes and the temperature behavior of the reservoir, despite the one-dimensional nature of the model that cannot account for energy inputs and outputs associated with reservoir operations. The best performance is achieved during the first few months after the ice break-up (mean error = −0.3 and −2.7 W m−2 for latent and sensible heat fluxes, respectively). The model overreacts to strong wind events, leading to subsequent poor estimates of water temperature and eventually to an early freeze-up. The model overestimated the measured annual evaporation corrected for the lack of energy balance closure by 5% and 16% in 2019 and 2020.

Significance Statement

Freshwater bodies impact the regional climate through energy and water exchanges with the atmosphere. It is challenging to model surface energy fluxes over a northern lake due to the succession of stratification and mixing periods over a year. This study focuses on the interactions between the atmosphere of an irregular shaped northern hydropower reservoir. Direct measurements of turbulent fluxes using an eddy covariance system allowed the model assessment. Turbulent fluxes were successfully predicted during the open water period. Comparison between observed and modeled time series showed a good agreement; however, the model overreacted to high wind episodes. Biases mostly occur during freeze-up and breakup, stressing the importance of a good representation of the ice cover processes.

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Wen-Shu Lin
,
Joel R. Norris
,
Michael J. DeFlorio
, and
F. Martin Ralph

Abstract

We apply the Ralph et al. scaling method to a reanalysis dataset to examine the climatology and variability of landfalling atmospheric rivers (ARs) along the western North American coastline during 1980–2019. The local perspective ranks AR intensity on a scale from 1 (weak) to 5 (strong) at each grid point along the coastline. The object-based perspective analyzes the characteristics of spatially independent and temporally coherent AR objects making landfall. The local perspective shows that the annual AR frequency of weak and strong ARs along the coast is highest in Oregon and Washington and lowest in Southern California. Strong ARs occur less frequently than weak ARs and have a more pronounced seasonal cycle. If those ARs with integrated water vapor transport (IVT) weaker than 250 kg m−1 s−1 are included, there is an enhanced seasonal cycle of AR frequency in Southern California and a seasonal cycle of AR intensity but not AR frequency in Alaska. The object-based analysis additionally indicates that strong ARs at lower latitudes are associated with stronger wind than weak ARs but similar moisture, whereas strong ARs at higher latitudes are associated with greater moisture than weak ARs but similar wind. For strong ARs, IVT at the core is largest for ARs in Oregon and Washington and smaller poleward and equatorward. Both IVT in the AR core and cumulative IVT along the coastline usually decrease after the first day of landfall for weak ARs but increase from the first to second day for strong ARs.

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Guo-Shiuan Lin
,
Ruben Imhoff
,
Marc Schleiss
, and
Remko Uijlenhoet

Abstract

Radar rainfall nowcasting has mostly been applied to relatively large (often rural) domains (e.g., river basins), although rainfall nowcasting in small urban areas is expected to be more challenging. Here, we selected 80 events with high rainfall intensities (at least one 1-km2 grid cell experiences precipitation >15 mm h−1 for 1-h events or 30 mm day−1 for 24-h events) in five urban areas (Maastricht, Eindhoven, The Hague, Amsterdam, and Groningen) in the Netherlands. We evaluated the performance of 9060 probabilistic nowcasts with 20 ensemble members by applying the short-term ensemble prediction system (STEPS) from Pysteps to every 10-min issue time for the selected events. We found that nowcast errors increased with decreasing (urban) areas especially when below 100 km2. In addition, at 30-min lead time, the underestimation of nowcasts was 38% larger and the discrimination ability was 11% lower for 1-h events than for 24-h events. A set of gridded correction factors for the Netherlands, CARROTS (Climatology-based Adjustments for Radar Rainfall in an Operational Setting) could adjust the bias in real-time QPE and nowcasts by 70%. Yet, nowcasts were still found to underestimate rainfall more than 50% above 40-min lead time relative to the reference, which indicates that this error originates from the nowcasting model itself. Also, CARROTS did not adjust the rainfall spatial distribution in urban areas much. In summary, radar-based nowcasting for urban areas (between 67 and 213 km2) in the Netherlands exhibits a short skillful lead time of about 20 min, which can only be used for last-minute warning and preparation.

Open access
Yusen Yuan
,
Lixin Wang
,
Zhongwang Wei
,
Hoori Ajami
,
Honglang Wang
, and
Taisheng Du

Abstract

The isotopic composition of evapotranspiration δ ET is a crucial parameter in isotope-based evapotranspiration (ET) partitioning and moisture recycling studies. The Keeling plot method is the most prevalent method to calculate δ ET, though it contains large extrapolated uncertainties from the least squares regression. Traditional Keeling regression uses the mean point of individual measurements. Here, a modified Keeling plot framework was proposed using the median point of individual measurements. We tested the δ ET uncertainty using the mean point [σ ET (mean)] and median point [σ ET (median)]. Multiple resolutions of input and output data from six independent sites were used to test the performance of the two methods. The σ ET (mean) would be greater than σ ET (median) when the mean value of inverse vapor concentration ( 1 / C υ ¯ ) is greater than the median value of inverse vapor concentration [ 1 / C υ ( median ) ]. When applying the filter of r 2 > 0.8, around 70% of σ ET (mean) was greater than σ ET (median). This phenomenon might be due to the normality of the vapor concentration Cυ producing the asymmetric distribution of 1/Cυ . The median method could perform significantly better than the mean method when inputting high-resolution measurements (e.g., 1 Hz) and when the water vapor concentration Cυ is relatively low. Compared to the mean method, applying the median method could on average reduce 6.88% of ET partitioning uncertainties and could on average reduce 9.00% of moisture recycling uncertainties. This study provided a new insight of the Keeling plot method and emphasized handling model output uncertainty from multiple perspectives instead of only from input parameters.

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Yuanyuan Zhou
and
Liang Gao

Abstract

The spatiotemporal variations of annual tropical-cyclone-induced rainfall (TCR) and non-tropical-cyclone-induced rainfall (NTCR) during 1960–2017 in Southeast China are investigated in this study. The teleconnections to sea surface temperature, the Arctic Oscillation, the Southern Oscillation, and the Indian Ocean dipole are examined. A significant decrease in annual TCR in the Pearl River basin was detected, while an increase in annual TCR in rainstorms was observed in the northeast of the Pearl River basin and south of the Yangtze River basin. A northward migration of a TCR belt was identified, which was also indicated by the pronounced anomalies of annual TCR. There was in general an increasing trend of non-tropical-cyclone-induced moderate rain, heavy rain, and rainstorms in Southeast China. Compared with the non-tropical-cyclone-induced heavy rain, the abnormal non-tropical-cyclone-induced rainstorms are more northerly. Both monthly TCR and NTCR were remarkably affected by the Arctic Oscillation, Southern Oscillation, and Indian Ocean dipole. TCR was more easily affected by the Arctic Oscillation compared to NTCR.

Significance Statement

Tropical-cyclone- and non-tropical-cyclone-induced rainfall (TCR and NTCR) prevails in Southeast China, and their characteristics of spatiotemporal variability are of significance in predicting rainfall over the study area. Therefore, this study aims to detect the degree to which rainfall varies in time and space, respectively, using the Mann–Kendall test and the empirical orthogonal function method. Moreover, to explore which climatic factor contributes the most to the spatiotemporal variability of TCR and NTCR, the teleconnections to the large-scale climatic indices including sea surface temperature, the Arctic Oscillation, the Southern Oscillation, and the Indian Ocean dipole are studied. The spatiotemporal variations of TCR and NTCR were affected by the sea surface temperature and the other three large-scale climatic indices. The findings in this study are expected to deepen the understanding of spatiotemporal variations of TCR and NTCR over Southeast China and the teleconnections to climatic indices.

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