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Zhuoyong Xiao
,
Xinping Zhang
,
Xiong Xiao
,
Xin Chang
, and
Xinguang He

Abstract

Convective/advective precipitation partitions refer to the divisions of precipitation that are either convective or advective in nature, relative to the total precipitation amount. These distinct partitions can have a significant influence on the stable isotope composition of precipitation. This study analyzed and compared the effect of precipitation partitions on δ 18O in precipitation (δ 18O p ) by using daily precipitation stable isotope data from Changsha station and monthly precipitation stable isotope data from the Global Network of Isotopes in Precipitation (GNIP), under different time scales, time intervals (i.e., annual, warm season, and cold season), and precipitation intensities. The results showed that the correlation between the convective precipitation fraction (CPF) and total precipitation amount was influenced by the intensity of convection in different time intervals. On both the daily and monthly scales, the CPF decreased as the precipitation amount increased in the warm season, while it increased with increasing precipitation amount in the cold season. Regardless of the season, daily δ 18O p at Changsha station consistently increased with an increase in daily CPF. On a daily scale, the effect of convective activity on δ 18O p was stronger than that of the “precipitation amount effect” in the cold season, as compared to the situation in the warm season. As a result, the regression line slope between δ 18O p and CPF increased with increasing precipitation intensity in the warm season, meaning that as the CPF increased, the δ 18O p increased at a faster rate under higher precipitation intensity. Similarly, the slope increased with increasing precipitation intensity in the cold season. This suggests that precipitation intensity and convection intensity can affect the relationship between δ 18O p and CPF. Our findings shed light on how different precipitation partitions affect stable isotope composition of precipitation, thus enhancing our understanding of the variability of precipitation stable isotopes in the monsoon regions of China.

Significance Statement

This study aims to better elucidate the influence of different precipitation partitions on precipitation stable isotopes. In the eastern monsoon region of China, stable isotopes in precipitation showed a robust positive relationship with convective precipitation faction. On a daily scale, the convective activity enhanced the influences of the “precipitation amount effect” on precipitation stable isotopes in the warm season and reduced such influences in the cold season. These results improve our understanding of stable isotopic variability of precipitation in the eastern monsoon region, China.

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Nika Tsitelashvili
,
Trent Biggs
,
Ye Mu
, and
Vazha Trapaidze

Abstract

Analyzing water resources in areas with few hydrometeorological stations, such as those in post-Soviet countries, is difficult due to station closures after 1989. In Caucasus, evaluations often rely on outdated data from nearby rivers. We evaluated one national-level precipitation dataset, the Water Balance of Georgia (WBG) with two satellite-based precipitation products from 1981 to 2021, including the Climate Hazards Group Infrared Precipitation with Station (CHIRPS) data and CHIRPS blended with a dense rain gauge network (geoCHIRPS). We modeled mean annual precipitation from geoCHIRPS as a function of coastal distance and elevation. CHIRPS underestimated precipitation in the cold and wet seasons (R 2 = 0.74, r = 0.86) and overestimated dry season precipitation, while geoCHIRPS performed well in all seasons (R 2 = 0.86, r = 0.92). Distance from the coast was a more important predictor of precipitation than elevation in western Georgia, while precipitation correlated positively with elevation in the east. At four hydroelectric plants, the underperformance as a percentage of capacity (∼37%) corresponds with the percentage difference between differences in precipitation products (∼38%), suggesting that plants designed based on WBG may be systematically overdesigned, but further work is needed to determine the reasons for the underperformance of the plants and frequency. We conclude that 1) the existing WBG does not accurately reflect elevation–precipitation relationships near the coast, and 2) for accurate analysis of spatiotemporal precipitation variability and its impacts on hydropower generation and environmental and sustainable water resource management, it is essential to calibrate satellite-based precipitation estimates with additional rain gauge data.

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Janice L. Bytheway
,
William R. Currier
,
Mimi Hughes
,
Kelly Mahoney
, and
Rob Cifelli

Abstract

Wintertime precipitation poses many observational and forecasting challenges, especially in the complex topography of the western United States where radar beam blockage and difficulty siting in situ observations yields more sparse observations than in the eastern United States. Uncertainty in western U.S. winter precipitation is known to be high, so much so that some studies have found model simulated precipitation to produce similar or better large-scale estimates of annual precipitation than gridded observational products during climatologically anomalous years. This study evaluates high-resolution gridded precipitation estimates from Multi-Radar Multi-Sensor (MRMS) and Stage IV as well as forecasts from NOAA’s High-Resolution Rapid Refresh (HRRR) model in the Colorado Rocky Mountains. Gridded precipitation estimates and forecasts are compared with in situ SNOTEL measurements for two seasons of wintertime precipitation. The influence of forecast length, lead time, and model elevation on seasonal precipitation predictions from the HRRR are investigated. Additional comparisons are made with the relatively dense network of observations deployed in Colorado’s East River Watershed during the Study of Precipitation, the Lower Atmosphere and Surface for Hydrometeorology (SPLASH) campaign. Gridded products and forecasts are found to underestimate cold-season precipitation by 25%–65% relative to in situ and aircraft measurements, with longer forecast periods and lead times (6–24 h) having smaller biases (25%–30%) than shorter forecast periods and lead times (55%–65%). The assessment of multiple years of observations indicates that these biases are related more to the data and methods used to create the gridded products and forecasts than to precipitation characteristics.

Significance Statement

In the mountainous western United States, it is very challenging to both observe and forecast wintertime precipitation, yet snowfall plays an important role in providing the region’s annual water supply. This study aims to increase our understanding of the biases in observations and forecasts of snowfall in the Colorado Rocky Mountains, which can in turn impact forecasts of water availability for the ensuing warm season. In this study we find high-resolution gridded precipitation estimates and forecasts to underestimate cold-season precipitation when compared with in situ observing stations, with longer-range forecasts (e.g., daily) being the least biased. These findings were consistent over two years of study and have broad implications for the hydrologic modeling and water management communities.

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Hoang Tran
,
Yilin Fang
,
Zeli Tan
,
Tian Zhou
, and
L. Ruby Leung

Abstract

The Lower Mississippi River Basin (LMRB) has experienced significant changes in land cover and is one of the most vulnerable regions to hurricanes in the United States. Here we study the impacts of land cover change on the hydrologic response to Hurricane Ida in LMRB. By using an integrated surface-subsurface hydrologic model, ELM-ParFlow, we simulate the effects of land cover change on the flood volume and peak timing induced by rainfall from Hurricane Ida. The results show that land cover changes from 1850 to 2015, which resulted in a smoother surface and less vegetation, exacerbated both flood peak time and volume induced by Hurricane Ida. The effects of land cover changes can be decomposed into two mechanisms: a smoother surface routes more water faster to a watershed outlet, and less vegetation allows more water to contribute to surface runoff. By comparing scenarios in which the two mechanisms were isolated, we found that changes in soil moisture due to vegetation cover change have more dominant effects on floods in the southern part and changes in Manning’s coefficient have the largest effect on floods in the northern part of the LMRB. The study provides important insights into the complex relationship between land use, land cover, and hydrologic processes in coastal regions.

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Olivier Champagne
,
Olga Zolina
,
Jean-Pierre Dedieu
,
Mareile Wolff
, and
Hans-Werner Jacobi

Abstract

The Svalbard archipelago, in the Atlantic-Arctic region, has been affected by a strong increase in precipitation in the last decades, with major potential impacts for the cryosphere, bio-geochemical cycles, and the ecosystems. Ny-Ålesund (79°N), in the northwest part of Svalbard, hosts invaluable meteorological records widely used by many researchers. Among the observed parameters, the amount of precipitation is subject to large biases, mainly due to the well-known precipitation gauges undercatch during windy conditions. The purpose of this study is to investigate if the observed trend of precipitation in Ny-Ålesund in the 1975-2022 period was real and how it was impacted by the gauge undercatch. We applied several correction factors developed in the last decades, based on local wind speed and temperature. We forced these corrections with 12-hourly precipitation data from the Ny-Ålesund weather station. Taking the period 1975-2022, the trend of precipitation increased from 3.8 mm/year in the observations to 4.5 mm/year (±0.2) after the corrections, mainly due to enhanced snowfall in November to January months. Taking the most recent 40 years period (1983-2022), the amount of precipitation still increased by 3.8 mm/year in the observations, but only by 2.6 mm/year (±0.5) after the corrections. The recent observed trend of precipitation stays large due to an increase of wet snowfall and rainfall that are measured more efficiently by the precipitation gauge. This result shows the need of applying corrections factors when using precipitation gauge data, especially in regions exhibiting large inter-annual changes of weather conditions.

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Kyle Lesinger
,
Di Tian
, and
Hailan Wang

Abstract

Flash droughts are rapid developing subseasonal climate extreme events that are manifested as suddenly decreased soil moisture, driven by increased evaporative demand and/or sustained precipitation deficits. Over each climate region in the contiguous United States (CONUS), we evaluated forecast skill of weekly root-zone soil moisture (RZSM), evaporative demand (ETo), and relevant flash drought (FD) indices derived from two dynamic models (GEOSV2p1 and GEFSv12) in the Subseasonal Experiment (SubX) project between years 2000-2019 against three reference datasets: MERRA-2, NLDAS-2, and GEFSv12 reanalysis. ETo and its forcing variables at lead week 1 have moderate to high anomaly correlation coefficient (ACC) skill (~0.70-0.95) except downwelling shortwave radiation, and by weeks 3-4 predictability was low for all forcing variables (ACC <0.5). RZSM (0-100cm) for model GEFSv12 showed high skill at lead week 1 (~0.7-0.85 ACC) in the High Plains, West, Midwest, and South CONUS regions when evaluated against GEFSv12 reanalysis but lower skill against MERRA-2 and NLDAS-2 and ACC skill are still close to 0.5 for lead weeks 3-4, better than ETo forecasts. GEFSv12 analysis has not been evaluated against in situ observations and has substantial RZSM anomaly differences when compared to NLDAS-2 and our analysis identified GEFSv12 reforecast prediction limit, which can maximally achieve ACC ~0.6 for RZSM forecasts between lead weeks 3-4. Analysis of major FD events reveal that GEFSv12 reforecast inconsistently captured the correct location of atmospheric and RZSM anomalies contributing to FD onset, suggesting the needs for improving the dynamic models’ assimilation and initialization procedures to improve subseasonal FD predictability.

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Linda Bogerd
,
Chris Kidd
,
Christian Kummerow
,
Hidde Leijnse
,
Aart Overeem
,
Veljko Petkovic
,
Kirien Whan
, and
Remko Uijlenhoet

Abstract

Spaceborne microwave radiometers represent an important component of the Global Precipitation Measurement (GPM) mission due to their frequent sampling of rain systems. Microwave radiometers measure microwave radiation (brightness temperatures, Tb), which can be converted into precipitation estimates with appropriate assumptions. However, detecting shallow precipitation systems using space-borne radiometers is challenging, especially over land, as their weak signals are hard to differentiate from those associated with dry conditions. This study uses a random forest model (RF) to classify microwave radiometer observations as dry, shallow, or non-shallow over the Netherlands - a region with varying surface conditions and frequent occurrence of shallow precipitation. The RF is trained on five years of data (2016-2020) and tested with two independent years (2015, 2021). The observations are classified using ground-based weather radar echo top heights. Various RF models are assessed, such as using only GPM’s Microwave Imager (GMI) Tb values as input features or including spatially aligned ERA-5 2-meter temperature and freezing level reanalysis and/or Dual Precipitation Radar (DPR) observations. Independent of the input features, the model performs best in summer and worst in winter. The model classifies observations from high-frequency channels (≥85 GHz) with lower Tb-values as non-shallow, higher values as dry, and those in between as shallow. Misclassified footprints exhibit radiometric characteristics corresponding to their assigned class. Case studies reveal dry observations misclassified as shallow are associated with lower Tb-values, likely resulting from the presence of ice particles in non-precipitating clouds. Shallow footprints misclassified as dry are likely related to the absence of ice particles.

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Joseph Sedlar
,
Tilden Meyers
,
Christopher J. Cox
, and
Bianca Adler

Abstract

Measurements of atmospheric structure and surface energy budgets distributed along a high-altitude mountain watershed environment near Crested Butte, Colorado, USA, from two separate, but coordinated, field campaigns, SAIL and SPLASH, are analyzed. This study identifies similarities and differences in how clouds influence the radiative budget over one snow-free summer season (2022) and two snow-covered seasons (2021-22; 2022-23) for this alpine location. A relationship between lower tropospheric stability stratification and longwave radiative flux from the presence or absence of clouds is identified. When low clouds persisted, often with signatures of supercooled liquid in winter, the lower troposphere experienced weaker stability, while radiatively clear skies that are less likely to be influenced by liquid droplets were associated with appreciably stronger lower tropospheric stratification. Corresponding surface turbulent heat fluxes partitioned differently based upon the cloud-stability stratification regime derived from early morning radiosounding profiles. Combined with the differences in the radiative budget largely resulting from dramatic seasonal differences in surface albedo, the lower atmosphere stratification, surface energy budget, and near-surface thermodynamics are shown to be modified by the effective longwave radiative forcing of clouds. The diurnal evolution of thermodynamics and surface energy components varied depending on early morning stratification state. Thus, the importance of quiescent versus synoptically-active large-scale meteorology is hypothesized as a critical forcing for cloud properties and associated surface energy budget variations. The physical relationships between clouds, radiation, and stratification can provide a useful suite of metrics for process-understanding and to evaluate numerical models in such an undersampled, highly complex terrain environment.

Open access
Free access
Maria Laura Poletti
,
Martina Lagasio
,
Antonio Parodi
,
Massimo Milelli
,
Vincenzo Mazzarella
,
Stefano Federico
,
Lorenzo Campo
,
Marco Falzacappa
, and
Francesco Silvestro

Abstract

Flood forecasting remains a significant challenge, particularly when dealing with basins characterized by small drainage areas (i.e., 103 km2 or lower with response time in the range 0.5–10 h) especially because of the rainfall prediction uncertainties. This study aims to investigate the performances of streamflow predictions using two short-term rainfall forecast methods. These methods utilize a combination of a nowcasting extrapolation algorithm and numerical weather predictions by employing a three-dimensional variational assimilation system, and nudging assimilation techniques, meteorological radar, and lightning data that are frequently updated, allowing new forecasts with high temporal frequency (i.e., 1–3 h). A distributed hydrological model is used to convert rainfall forecasts into streamflow prediction. The potential of assimilating radar and lightning data, or radar data alone, is also discussed. A hindcast experiment on two rainy periods in the northwest region of Italy was designed. The selected skill scores were analyzed to assess their degradation with increasing lead time, and the results were further aggregated based on basin dimensions to investigate the catchment integration effect. The findings indicate that both rainfall forecast methods yield good performance, with neither definitively outperforming the other. Furthermore, the results demonstrate that, on average, assimilating both radar and lightning data enhances the performance.

Open access