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Abby Hutson
,
Ayumi Fujisaki-Manome
, and
Brent Lofgren

Abstract

The Weather Research and Forecasting (WRF) Model is used to dynamically downscale 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.

Significance Statement

Global climate models are vital to understanding our changing climate. While many include a coarse representation of the Great Lakes, they lack the resolution to represent effects like lake effect precipitation, lake breeze, and surface air temperature modification. Therefore, using a regional climate model to downscale global data is imperative to correctly simulate the land–lake–atmosphere feedbacks that contribute to regional climate. Modeling precipitation is particularly important because it plays a direct role in the Great Lakes’ water cycle. The purpose of this study is to identify the configuration of the Weather Research and Forecasting Model that best simulates precipitation and temperature in the Great Lakes region by testing cumulus parameterizations and methods of nudging the regional model toward the global model.

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 2008–15 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 probabilistic 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.

Significance Statement

We show how to use different precipitation estimates, like computer simulations of weather or satellite observations, in conjunction with all available ground measurements in regions with generally poor meteorological and flow measurement infrastructure. We demonstrate how it is possible to retrieve past unobserved river flows using these estimates in combination with a hydrological computer model for streamflow simulations. The method can help us to better understand the hydrology of poorly gauged regions that play an important role in the distribution of water resources and can be affected by future changes. We applied the method to a large transboundary river basin in China. This basin holds water needed by large, densely populated regions of India that may become water constrained by warmer climate.

Open access
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 spaceborne 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 (RF) model to classify microwave radiometer observations as dry, shallow, or nonshallow over the Netherlands—a region with varying surface conditions and frequent occurrence of shallow precipitation. The RF model is trained on five years of data (2016–20) and tested with two independent years (2015 and 2021). The observations are classified using ground-based weather radar echo top heights. Various RF models are assessed, such as using only GPM Microwave Imager (GMI) Tb values as input features or including spatially aligned ERA5 2-m temperature and freezing level reanalysis and/or Dual-Frequency 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 nonshallow, 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 nonprecipitating clouds. Shallow footprints misclassified as dry are likely related to the absence of ice particles.

Significance Statement

Published research concerning rainfall retrieval algorithms from microwave radiometers is often focused on the accuracy of these algorithms. While shallow precipitation over land is often characterized as problematic in these studies, little progress has been made with these systems. In particular, precipitation formed by shallow clouds, where shallow refers to the clouds being close to Earth’s surface, is often missed. This study is focused on detecting shallow precipitation and its physical characteristics to further improve its detection from spaceborne sensors. As such, it contributes to understanding which shallow precipitation scenes are challenging to detect from microwave radiometers, suggesting possible ways for algorithm improvement.

Open access
Anju Vijayan Nair
,
Sungwook Wi
,
Rijan Bhakta Kayastha
,
Colin Gleason
,
Ishrat Dollan
,
Viviana Maggioni
, and
Efthymios I. Nikolopoulos

Abstract

Hydrologic assessment of climate change impacts on complex terrains and data-sparse regions like High Mountain Asia is a major challenge. Combining hydrological models with satellite and reanalysis data for evaluating changes in hydrological variables is often the only available approach. However, uncertainties associated with the forcing dataset, coupled with model parameter uncertainties, can have significant impacts on hydrologic simulations. This work aims to understand and quantify how the uncertainty in precipitation and its interaction with the model uncertainty affect streamflow estimation in glacierized catchments. Simulations for four precipitation datasets [Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG), Climate Hazards Group Infrared Precipitation with Station (CHIRPS), ERA5-Land, and Asian Precipitation–Highly Resolved Observational Data Integration Toward Evaluation (APHRODITE)] and two glacio-hydrological models [Glacio-Hydrological Degree-Day Model (GDM) and Hydrological Model for Distributed Systems (HYMOD_DS)] are evaluated for the Marsyangdi and Budhigandaki River basins in Nepal. Temperature sensitivity of streamflow simulations is also investigated. Relative to APHRODITE, which compared well with ground stations, ERA5-Land overestimates the catchment average precipitation for both basins by more than 70%; IMERG and CHIRPS overestimate by ∼20%. Precipitation uncertainty propagation to streamflow exhibits strong dependencies to model structure and streamflow components (snowmelt, ice melt, and rainfall-runoff), but overall uncertainty dampens through precipitation-to-streamflow transformation. Temperature exerts a significant additional source of uncertainty in hydrologic simulations of such environments. GDM was found to be more sensitive to temperature variations, with >50% increase in total flow for 20% increase in actual temperature, emphasizing that models that rely on lapse rates for the spatial distribution of temperature have much higher sensitivity. Results from this study provide critical insight into the challenges of utilizing satellite and reanalysis products for simulating streamflow in glacierized catchments.

Significance Statement

This work investigates the uncertainty of streamflow simulations due to climate forcing and model parameter/structure uncertainty and quantifies the relative importance of each source of uncertainty and its impact on simulating different streamflow components in glacierized catchments of High Mountain Asia. Results highlight that in high mountain regions, temperature uncertainty exerts a major control on hydrologic simulations and models that do not adequately represent the spatial variability of temperature are more sensitive to bias in the forcing data. These findings provide guidance on important aspects to be considered when modeling glacio-hydrological response of catchments in such areas and are thus expected to impact both research and operation practice related to hydrologic modeling of glacierized catchments.

Open access
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, from two separate, but coordinated, field campaigns, Surface Atmosphere Integrated field Laboratory (SAIL) and Study of Precipitation, the Lower Atmosphere, and Surface for Hydrometeorology (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 the 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
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
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
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
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
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