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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
Paolo Reggiani
and
Oleksiy Boyko

Abstract

We study the impact of uncertain precipitation estimates on simulated streamflows for the poorly gauged Yarlung Tsangpo basin (YTB), High Mountain Asia (HMA). A process-based hydrological model at 0.5 km resolution is driven by an ensemble of precipitation estimation products (PEPs), including analyzed ground observations, high-resolution precipitation estimates, climate data records and reanalyses over the 2008-2015 control period. The model is then forced retrospectively from 1983 onward to obtain seamless discharge estimates till 2007, a period for which there is very sparse flow data coverage. Whereas temperature forcing is considered deterministic, precipitation is sampled from the predictive distribution, which is obtained through processing PEPs by means of a probabilisitc processor of uncertainty. The employed Bayesian processor combines the PEPs and outputs the predictive densities of daily precipitation depth accumulation as well as the probability of precipitation occurrence, from which random precipitation fields for probabilistic model forcing are sampled. The predictive density of precipitation is conditional on the precipitation estimation predictors that are bias-corrected and variance adjusted. For the selected HMA study site, discharges simulated from reanalysis and climate data records score lowest against observations at three flow gauging points, whereas high-resolution satellite estimates perform better, but are still outperformed by precipitation fields obtained from analyzed observed precipitation and merged products, which were corrected against ground observations. The applied methodology indicates how missing flows for poorly gauged sites can be retrieved and is further extendable to hydrological projections of climate.

Open access
Xiaodong Hong
and
Qingfang Jiang

Abstract

The impact of land surface snow processes on the Arctic stable boundary layer (ASBL) is investigated using the Navy’s Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS) to reduce the cold bias caused by decoupling between the land surface and atmosphere. The Noah land surface model (LSM) with improved snow processes is examined using COAMPS forecast forcing in the one-dimensional mode for one month. The new snow physics shows that the snow properties, roughness length, and sensible heat flux are modified as expected to compensate for the old LSM deficiency. These new snow processes are incorporated into the COAMPS Noah LSM, and the 48-h forecasts using both old and new Noah LSMs are performed for January 2021 with an every-6-h data assimilation update cycle. Standard verifications of the 48-h forecasts have used all available observational datasets and the snow depth from the Land Information System (LIS) analyses. The statistics have shown reduced monthly mean cold biases ∼1°C by the new snow physics. The weaker strength of surface inversion and stronger turbulence kinetic energy (TKE) from the new snow physics provides a higher boundary layer due to significantly stronger eddy mixing. The simulations have also shown the insignificant impact of different lateral boundary conditions obtained from the global forecasts or analyses on the results of the new snow physics. This study highlights the importance of the revised snow physics in Noah LSM for reducing the decoupling problem, improving the forecasts, and studying ASBL physics over the Arctic region.

Open access
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 d−1 for 24-h events) in five urban areas (Maastricht, Eindhoven, The Hague, Amsterdam, and Groningen) in the Netherlands. We evaluated the performance of 9,060 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 compared 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 minutes, which can only be used for last-minute warning and preparation.

Open access
Savannah K. Jorgensen
and
John W. Nielsen-Gammon

Abstract

This study estimates extreme rainfall trends across the Gulf and Southeastern Coasts of the US 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 model surface temperature (GMST) as the covariate. County composites and multi-county 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 19% (5%, 33%) for the 2-yr return period and 14% (4%, 24%) 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.

Open access
Haochen Tan
,
Rao Kotamarthi
, and
Pallav Ray

Abstract

The surface sensible heat flux induced by precipitation (QP ) is a consequence of the temperature difference between the surface and the rain droplets. Despite its seemingly negligible nature, QP is frequently omitted from both meteorological and climatological models. Nevertheless, it is important to acknowledge the numerous occasions in which the instantaneous values of QP can be significant, particularly during extreme precipitation events. This study undertakes a comprehensive assessment of QP across the contiguous United States (CONUS) utilizing high-resolution reanalysis, observational data, and numerical modeling to examine the influence of QP on precipitation and the surface energy budget. The findings indicate that the spatial distribution of QP climatology is analogous to that of precipitation, with magnitudes ranging from 2 to 3 W m−2 predominantly over the Midwest and Southeast regions. A seasonal analysis of QP reveals that the highest values occurring during the June–August (JJA) period, averaging 3.18 W m−2. Peak QP values of approximately 4 W m−2 are observed during JJA over the Great Plains region. We hypothesize that the QP during an extreme precipitation event would be nonnegligible and have a significant impact on the local weather. To test this conjecture, we perform high-resolution simulations with and without QP during an extreme precipitation event over the Chicago Metropolitan Area (CMA). The results show that the QP may be a dominant factor compared to other components of surface heat flux during the zenith of precipitation hours. Also, QP has the potential to not only diminish precipitation but also alter and reconfigure the remaining surface energy budget components.

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

Abstract

Hydrological predictions at sub-seasonal to seasonal (S2S) timescales 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 three-month hydrological forecasts for the austral autumn season (March–April–May) using ensemble hindcasts for 2002-2017. Results indicate that the S2S-HFS generally offers skill in predictions of monthly precipitation up to one month lead, evapotranspiration up to 2 months lead, and soil moisture content up to three 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: the El Niño Southern Oscillation (ENSO) and the Antarctic Oscillation (AAO). We find that forecast skill for all variables at one 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
Juliana Valencia
,
Johanna Yepes
,
John F. Mejía
,
Alejandro Builes-Jaramillo
, and
Hernán D. Salas

Abstract

This study investigates how convectively coupled tropical easterly waves (TEWs) affect the Choco low-level jet (ChocoJet) as they move across the western Caribbean. The ChocoJet is a low-level flow over the eastern Pacific (EPAC) that modulates precipitation patterns over the tropical eastern Pacific and northwestern South America. By combining data from the Organization of Tropical East Pacific Convection (OTREC; August–September 2019), ERA5 reanalysis products, and satellite data, we analyze precipitation and circulation patterns during convectively coupled and nonconvectively coupled TEWs, comparing them to non-TEW days. During convectively coupled TEWs days, the ChocoJet strengthens and becomes more southerly, while the ITCZ moves northward, leading to enhanced precipitation over the western Caribbean and drier conditions over the northern part of the Colombian Pacific. In contrast, nonconvectively coupled TEW days exhibit reduced precipitation and precipitable water over the Caribbean and far EPAC, with a layer of northeasterly flow centered at 850 hPa flowing over a shallower, weaker, and more westerly ChocoJet. Additionally, convectively coupled TEWs are associated with a weaker western Caribbean and far eastern Pacific pressure gradient compared to nonconvective TEWs. These observable and predictable synoptic-scale circulation–precipitation relationships contribute to a better understanding of hydrometeorological variability in the region.

Significance Statement

Tropical easterly waves and related convective organization traversing the Caribbean Sea are important sources of synoptic-scale precipitation–circulation variability in the far eastern Pacific and Colombian Pacific. This eastern tropical Pacific study aims to identify precipitation–circulation relationships that enhance the understanding of synoptic-scale meteorological phenomena.

Open access