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Abby Hutson
,
Ayumi Fujisaki-Manome
, and
Brent Lofgren

Abstract

The Weather Research and Forecasting (WRF) model is used to dynamically down-scale ERA-Interim global reanalysis data to test its performance as a regional climate model (RCM) for the Great Lakes Region (GLR). Four cumulus parameterizations and three spectral nudging techniques applied to moisture are evaluated based on 2 m temperature and precipitation accumulation in the Great Lakes Drainage Basin (GLDB). Results are compared to a control simulation without spectral nudging, and additional analysis is presented showing the contribution of each nudged variable to temperature, moisture, and precipitation. All but one of the RCM test simulations have a dry precipitation bias in the warm months, and the only simulation with a wet bias also has the least precipitation error. It is found that the inclusion of spectral nudging of temperature dramatically improves a cold-season cold bias, and while the nudging of moisture improves simulated annual and diurnal temperature ranges, its impact on precipitation is complicated.

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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
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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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Mingze Ding
,
Zhehui Shen
,
Ruochen Huang
,
Ying Liu
, and
Hao Wu

Abstract

Evaluating the accuracy of various precipitation datasets over ungauged or even sparse-gauge areas is a challenging task. Cross-validation methods can evaluate three or more datasets based on the error independence from input data, without relying on ground reference. Here, the triple collocation (TC) method is employed to evaluate multi-source precipitation datasets: gauge-based CGDPA, model-based ERA5, and satellite-derived IMERG-Early, IMERG-Late, GSMaP-NRT, and GSMaP-MVK over the Tibetan Plateau (TP). TC-based results show that ERA5 has better performances than satellite-only precipitation products over mountainous regions with complex terrains. For purely satellite-derived products, IMERG products outperform GSMaP products. Considering the potential existence of error dependency among input datasets, caution should be exercised. Thus, it is necessary to introduce an alternative cross-validation method (generalized Three-Cornered Hat) and explore the applicability of cross-validation from the perspective of error independence. We found that cross-validation methods have high applicability in most TP regions with sparse-gauge density (accounting for about 80.1% of the total area). Additionally, we conducted simulation experiments to discuss the applicability and robustness of TC. The simulation results substantiated that cross-validation can serve as a robust evaluation method over sparse-gauge regions. Although it is generally known that the cross-validation methods can be served in sparse-gauge regions, the application condition of different evaluation methods for precipitation products is identified quantitatively in TP now.

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Zhehui Shen
,
Bin Yong
, and
Hao Wu

Abstract

Climatological calibration algorithm (CCA) and satellite–gauge combination (SG) are two official bias adjustments for satellite precipitation estimates (SPE) in the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA). The CCA is designed for the near-real-time SPEs, while the SG procedure is a final step to merge pure SPEs with gauge observations. This study explored the impacts of CCA and SG on the systematic and random errors of TMPA SPEs. The errors of TMPA version-7 near-real-time products before and after CCA (RT_UC, RT_C), and the research product TMPA 3B42 (V7), were decomposed into systematic and random components, benchmarked by the China Gauge-based Daily Precipitation Analysis (CGDPA). After being calibrated by CCA, RT_C reduced the systematic errors relative to RT_UC over the Chinese mainland, except in the Tibetan Plateau and Tianshan Mountains. However, CCA did not aid in reducing random errors; instead, it even exacerbated the random errors. On the other hand, the SG merging is more effective in reducing systematic errors of SPEs than CCA calibration because of the direct inclusion of simultaneous gauge data from the Global Precipitation Climatology Centre (GPCC). We also found that SG merging reduced the random errors of pure SPEs over regions with relatively higher elevations. Despite lower random errors in V7 over the complex terrain region, the SG unfavorably increased the random errors over southeastern China. The results reported here may offer valuable insights into the effects of CCA and SG techniques drawn from TMPA, with the potential to advance the development of bias-adjusting algorithms for SPEs in the future.

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Jessica R. P. Sutton
,
Dalia Kirschbaum
,
Thomas Stanley
, and
Elijah Orland

Abstract

Accurately detecting and estimating precipitation at near real-time (NRT) is of utmost importance for early detection and monitoring of hydrometeorological hazards. The precipitation product, Integrated Multi-satellitE Retrievals for the Global Precipitation Mission (IMERG), provides NRT 0.1° and 30-minute precipitation estimates across the globe with only a 4-hour latency. This study was an evaluation of the GPM IMERG version 6 level-3 Early Run 30-minute precipitation product for precipitation events from 2014 through 2020. The purpose of this research was to identify when, where, and why GPM IMERG misidentified and failed to detect precipitation events in California, Nevada, Arizona, and Utah in the United States. Precipitation events were identified based on 15-minute precipitation from gauges and 30-minute precipitation from the IMERG multi-satellite constellation. False positive and false negative precipitation events were identified and analyzed to determine characteristics. Precipitation events identified by gauges had longer duration and had higher cumulative precipitation than those identified by GPM IMERG. GPM IMERG had many false event detections during the summer months suggesting possible virga event detection, which is when precipitation falls from a cloud but evaporates before it reaches the ground. The frequency and timing of the merged Passive Microwave (PMW) product and forward propagation were responsible for IMERG overestimating cumulative precipitation during some precipitation events and underestimating others. This work can inform experts that are using the GPM IMERG NRT product to be mindful of situations where GPM IMERG estimated precipitation events may not fully resolve the hydrometeorological conditions driving these hazards.

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