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Belinda Trotta

Abstract

Ensemble Copula Coupling (Schefzik et al. 2013) is a widely used method to produce a calibrated ensemble from a calibrated probabilistic forecast. This process improves the statistical accuracy of the ensemble; in other words, the distribution of the calibrated ensemble members at each grid point more closely approximates the true expected distribution. However, the trade-off is that the individual members are often less physically realistic than the original ensemble: there is noisy variation among neighboring grid points, and, depending on the calibration method, extremes in the original ensemble are sometimes muted. We introduce Neighborhood Ensemble Copula Coupling (N-ECC), a simple modification of ECC designed to mitigate these problems. We show that, when used with the calibrated forecasts produced by Flowerdew’s (Flowerdew 2014) reliability calibration, N-ECC improves both the visual plausibility and the statistical properties of the forecast.

Restricted access
Ali Fallah
,
Mathew A. Barlow
,
Laurie Agel
,
Junghoon Kim
,
Justin Mankin
,
David M. Mocko
, and
Christopher B. Skinner

Abstract

Predicting and managing the impacts of flash droughts is difficult owing to their rapid onset and intensification. Flash drought monitoring often relies on assessing changes in root-zone soil moisture. However, the lack of widespread soil moisture measurements means that flash drought assessments often use process-based model data like that from the North American Land Data Assimilation System (NLDAS). Such reliance opens flash drought assessment to model biases, particularly from vegetation processes. Here we examine the influence of vegetation on NLDAS-simulated flash drought characteristics by comparing two experiments covering 1981-2017: open loop, (OL) which uses NLDAS surface meteorological forcing to drive a land-surface model using prognostic vegetation, and data assimilation (DA), which instead assimilates near-real-time satellite-derived leaf area index (LAI) into the land-surface model. The OL simulation consistently underestimates LAI across the U.S., causing relatively high soil moisture values. Both experiments produce similar geographic patterns of flash droughts, but OL produces shorter duration events and regional trends in flash drought occurrence that are sometimes opposite to those in DA. Across the Midwest and Southern U.S., flash droughts are four weeks (about 70%) longer on average in DA than OL. Moreover, across much of the Great Plains, flash drought occurrence has trended upward according to the DA experiment, opposite to the trend in OL. This sensitivity of flash drought to the representation of vegetation suggests that representing plants with greater fidelity could aid in monitoring flash droughts and improve the prediction of flash drought transitions to more persistent and damaging long-term droughts.

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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 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
Emiel van der Plas
,
Aart Overeem
,
Jan Fokke Meirink
,
Hidde Leijnse
, and
Linda Bogerd

Abstract

A new pan-European climatological dataset was recently released that has a much higher spatiotemporal resolution than existing pan-European interpolated rain gauge datasets. This radar dataset of hourly precipitation accumulations, EURADCLIM (Overeem et al. 2023), covers most of continental Europe with a resolution of 2 km × 2 km, and is adjusted employing data from potentially thousands of government rain gauges. This study aims to use this dataset to evaluate two important satellite-derived precipitation products over the period 2013 to 2019 at a much higher spatiotemporal resolution than was previously possible at the European scale: the IMERG late run and the Meteosat Second Generation (MSG) Cloud Physical Properties product from the SEVIRI instrument. The latter is only available during daytime, so the analyses are restricted to daytime conditions. A direct grid cell comparison of hourly precipitation reveals an apparently low coefficient of correlation. However, looking into slightly more detail at statistics pertaining to longer time scales or specific areas, the datasets show good correspondence. All datasets are shown to have their specific biases, that can be transient or more systematic, depending on the timing or location. The MSG precipitation seems to have an overall positive bias and the IMERG dataset suffers from some transient overestimation of certain events.

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Huancui Hu
,
L. Ruby Leung
,
Zhe Feng
, and
James Marquis

Abstract

Moisture recycling, the contribution of local evapotranspiration (ET) to precipitation, has been studied using bulk models assuming a well-mixed atmosphere. The latter is inconsistent with a climatologically stratified atmosphere that slants across latitudes. Reconciling the two views requires an understanding of overturning associated with different weather systems. In this study, we aim to better understand moisture recycling associated with mesoscale convective systems (MCSs). Using a convection-permitting WRF simulation equipped with water vapor tracers (WRF-WVT), we tag moisture from terrestrial ET in the U.S. Southern Great Plains during May 2015, when more than 20 MCS events occurred and produced significant precipitation and flooding. Water budget analysis reveals that approximately 76% of terrestrial ET is advected away from the region while the remaining 24% of terrestrial ET is “pumped” upward within the region, accounting for 12% of precipitation. Moisture recycling peaks during early night hours (1800–2400 LT) due to the mixing of the daytime accumulated ET by active convection. By focusing on five “diurnally driven” MCSs with less large-scale circulation influence than other MCSs during the same period, we find an upright pumping of terrestrial ET at the MCS initiation and development stages, which diverges into two branches during the MCS mature and decaying stages. One branch in the upper level advects the ET-sourced moisture downstream, while the other branch in the mid-to-upper level contributes to the trailing precipitation upstream. Overall, our analysis depicts a pumping mechanism associated with MCSs that mixes local ET vertically, highlighting its specific contributions to enhancing convective precipitation processes.

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Ondřej Lhotka
,
Eva Plavcová
, and
Romana Beranová

Abstract

We analyzed regional patterns of day-to-day precipitation variability across Europe and assessed their future changes using CORDEX regional climate models. A discrete Markov chain process was employed to calculate transition probabilities from wet and dry states and the precipitation variability was quantified using the proposed Variability index (IVAR; sum of wet-to-dry and dry-to-wet transitions divided by total number of transitions). IVAR is, in general, lowest in Southern Europe and gradually increases northward in the observed data. Performance of the regional climate models is season dependent: they capture IVAR reasonably well in summer but higher simulated variability was found for the winter season. IVAR trends computed for the 2006–2095 period suggest decreasing day-to-day precipitation variability over Southern Europe, especially in summer under the high-concentration RCP8.5 pathway. By contrast, increased variability is projected in Northern Europe. Between these two regions, future IVAR trends are less clear, because they strongly depend on the selection of driving global model, hinting of an uncertain future hydroclimate in the Central European region.

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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
Md Murad Hossain Khondaker
and
Mostafa Momen

Abstract

Hurricanes have been the most destructive and expensive hydro-meteorological event in US history, causing catastrophic winds and floods. Hurricane dynamics can significantly impact the amount and spatial extent of storm precipitation. However, the complex interactions of hurricane intensity and precipitation and the impacts of improving hurricane dynamics on streamflow forecasts are not well established yet. This paper addresses these gaps by comprehensively characterizing the role of vertical diffusion in improving hurricane intensity and streamflow forecasts under different planetary boundary layer, microphysics, and cumulus parameterizations. To this end, the Weather and Research Forecasting (WRF) atmospheric model is coupled with the WRF hydrological model (WRF-Hydro) to simulate four major hurricanes landfalling in three hurricane-prone regions in the US. First, a stepwise calibration is carried out in WRF-Hydro, which remarkably reduces streamflow forecast errors compared to the United States Geological Survey (USGS) gauges. Then, 60 coupled hydro-meteorological simulations were conducted to evaluate the performance of current weather parameterizations. All schemes were shown to underestimate the observed intensity of the considered major hurricanes since their diffusion is over-dissipative for hurricane flow simulations. By reducing the vertical diffusion, hurricane intensity forecasts were improved by ~39.5% on average compared to the default models. These intensified hurricanes generated more intense and localized precipitation forcing. This enhancement in intensity led to ~16% and ~34% improvements in hurricane streamflow bias and correlation forecasts, respectively. The research underscores the role of improved hurricane dynamics in enhancing flood predictions and provides new insights into the impacts of vertical diffusion on hurricane intensity and streamflow forecasts.

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David J. Lorenz
,
Jason A. Otkin
,
Benjamin F. Zaitchik
,
Christopher Hain
,
Thomas R. H. Holmes
, and
Martha C. Anderson

Abstract

The effect of machine learning and other enhancements on statistical-dynamical forecasts of soil moisture (0-10cm and 0-100cm) and a reference evapotranspiration fraction (Evaporative Stress Index, ESI) on sub-seasonal time scales (15-28 days) are explored. The predictors include the current and past land surface conditions, and dynamical model hindcasts from the Sub-seasonal to Seasonal (S2S) Prediction Project. When the methods are enhanced with machine learning and other improvements, the increases in skill are almost exclusively coming from predictors drawn from observations of current and past land surface states. This suggests that operational S2S flash drought forecasts should focus on optimizing use of information on current conditions rather than on integrating dynamically based forecasts, given the current state of knowledge. Nonlinear machine learning methods lead to improved skill over linear methods for soil moisture but not for ESI. Improvements for both soil moisture and ESI are realized by increasing the sample size by including surrounding grid points in training and increasing the number of predictors. In addition, all the improvements in the soil moisture forecasts predominantly impact soil moistening rather than soil drying—i.e., prediction of conditions moving away from drought rather than into drought—especially when the initial soil state is drier than normal. The physical reasons for the nonlinear machine learning improvements are also explored.

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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, 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