Journal of Hydrometeorology

doi: 10.1175/jhm-258mastheadpmid: N/A

Aguirre-Correa, Francisca; de Arellano, Jordi Vilà-Guerau; Ronda, Reinder; Lobos-Roco, Felipe; Suárez, Francisco; Hartogensis, Oscar

doi: 10.1175/jhm-d-23-0105.1pmid: N/A

AbstractObservations over a saltwater lagoon in the Altiplano show that evaporation E is triggered at noon, concurrent to the transition of a shallow, stable atmospheric boundary layer (ABL) into a deep mixed layer. We investigate the coupling between the ABL and E drivers using a land–atmosphere conceptual model, observations, and a regional model. Additionally, we analyze the ABL interaction with the aerodynamic and radiative components of evaporation using the Penman equation adapted to saltwater. Our results demonstrate that nonlocal processes are dominant in driving E. In the morning, the ABL is controlled by the local advection of warm air (∼5 K h−1), which results in a shallow (<350 m), stable ABL, with virtually no mixing and no E (<50 W m−2). The warm-air advection ultimately connects the ABL with the residual layer above, increasing the ABL height h by ∼1 km. At midday, a thermally driven regional flow arrives to the lagoon, which first advects a deeper ABL from the surrounding desert (∼1500 m h−1) that leads to an extra ∼700-m h increase. The regional flow also causes an increase in wind (∼12 m s−1) and an ABL collapse due to the entrance of cold air (∼−2 K h−1) with a shallower ABL (∼−350 m h−1). The turbulence produced by the wind decreases the aerodynamic resistance and mixes the water body releasing the energy previously stored in the lake. The ABL feedback on E through vapor pressure enables high evaporation values (∼450 W m−2 at 1430 LT). These results contribute to the understanding of E of water bodies in semiarid conditions and emphasize the importance of understanding ABL processes when describing evaporation drivers.

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

doi: 10.1175/jhm-d-23-0222.1pmid: N/A

AbstractEvaluating 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 observations. Here, the triple collocation (TC) method is employed to evaluate multisource precipitation datasets: China Gauge-based Daily Precipitation Analysis (CGDPA), model-based ERA5, and satellite-derived IMERG-Early, IMERG-Late, GSMaP in near–real time (GSMaP-NRT), and GSMaP moving vector with Kalman filter (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 dependencies 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.Significance StatementCross validation is a powerful assessment method for multisource precipitation datasets. This method is widely used by hydrologists in sparse-gauge or ungauged regions, like the African continent and the Tibetan Plateau (TP). However, as an indirect assessment method, the inherent uncertainty in cross validation warrants emphasis. Here, two cross-validation methods (the triple collocation method and the generalized three-cornered hat method) are employed to evaluate multisource precipitation datasets: China Gauge-based Daily Precipitation Analysis (CGDPA), model-based ERA5, and satellite-derived IMERG-Early, IMERG-Late, GSMaP in near–real time (GSMaP-NRT), and GSMaP moving vector with Kalman filter (GSMaP-MVK) over the TP. In this study, we not only assessed the current mainstream six precipitation datasets but also analyzed their uncertainties by combining these two cross-validation methods.

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

doi: 10.1175/jhm-d-23-0074.1pmid: N/A

AbstractThe effect of machine learning and other enhancements on statistical–dynamical forecasts of soil moisture (0–10 and 0–100 cm) and a reference evapotranspiration fraction [evaporative stress index (ESI)] on subseasonal time scales (15–28 days) are explored. The predictors include the current and past land surface conditions and dynamical model hindcasts from the Subseasonal to Seasonal Prediction project (S2S). 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.Significance StatementRapidly intensifying droughts pose extra challenges for predictability. Here, dynamical forecast model output is combined with nonlinear machine learning methods to improve forecasts of rapid changes in soil moisture and the evaporative stress index (ESI).

Lhotka, Ondřej; Plavcová, Eva; Beranová, Romana

doi: 10.1175/jhm-d-23-0206.1pmid: N/A

AbstractWe analyzed regional patterns of day-to-day precipitation variability across Europe and assessed their future changes using Coordinated Regional Climate Downscaling Experiment (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, the sum of wet-to-dry and dry-to-wet transitions divided by the total number of transitions). The 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. The IVAR trends computed for the 2006–95 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.Significance StatementIn a warming world, water availability will play a key role in ecosystem productivity. Although future changes in rainfall amounts have been studied extensively, much less attention has been given to changes in their temporal distribution and variability. Because grouping wet or dry days into sequences vitally contributes to characterizing floods or droughts, we aimed to study future changes in these tendencies. We found that although future changes in wet or dry days grouping tendencies are mostly driven solely by change in their frequency, climate models do not agree on the change in the frequency of wet days over large parts of continental Europe. This leaves major uncertainties in a future European hydroclimate and implications for impact modeling.

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

doi: 10.1175/jhm-d-23-0184.1pmid: N/A

AbstractA 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, European Radar Climatology (EURADCLIM) (Overeem et al.), 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 2013–19 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 property product from the SEVIRI instrument. The latter is only available during daytime, so the analyses are restricted to daytime conditions. A direct gridcell 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, which 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.

Kumar, Kondapalli Niranjan; Gupta, Ankur; Mohan, T. S.; Mishra, Akhilesh Kumar; Ashrit, Raghavendra; Momin, Imranali M.; Mahapatra, Debasis K.; Nagarjuna Rao, D.; Mitra, Ashis K.; Prasad, V. S.; Rajeevan, M.

doi: 10.1175/jhm-d-23-0171.1

Trotta, Belinda

doi: 10.1175/jhm-d-23-0188.1pmid: N/A

AbstractEnsemble copula coupling (Schefzik et al.) 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) reliability calibration, N-ECC improves both the visual plausibility and the statistical properties of the forecast.Significance StatementNumerical weather prediction (NWP) uses physical models of the atmosphere to produce a set of scenarios (called an ensemble) describing possible weather outcomes. These forecasts are used in other models to produce weather forecasts and warnings of extreme events. For example, NWP forecasts of rainfall are used in hydrological models to predict the probability of flooding. However, the raw NWP forecasts require statistical postprocessing to ensure that the range of scenarios they describe accurately represents the true range of possible outcomes. This paper introduces a new method of processing NWP forecasts to produce physically realistic, well-calibrated ensembles.

Shejule, Priya Ashok; Pekkat, Sreeja

doi: 10.1175/jhm-d-23-0173.1pmid: N/A

AbstractAmong all hydrometeorological parameters, rainfall strongly correlates with hydrometeorological disasters. The rainfall forecast process remains challenging due to the nonlinear, nonstationary nature and multiscale variability of rainfall. Moreover, the unique microclimate in different regions further complicates the forecasting process. This study proposes a hybrid model employing multivariate singular spectrum analysis (MSSA) and long short-term memory (LSTM) for multistep-ahead hourly rainfall forecasting in urban areas of northeast India. The model is trained and evaluated using high-resolution (12 km) hourly meteorological data from the Indian Monsoon Data Assimilation and Analysis (IMDAA) dataset for Guwahati (plain) and Aizawl (hilly) regions from 2015 to 2019. The hybrid model outperforms the single LSTM model in both plain and hilly regions, with an average percentage gain of 47.99% and 43.88% for symmetric mean absolute percentage error (SMAPE) and root-mean-square error (RMSE) in the case of the Guwahati dataset and 84.59% and 82.27% in the case of the Aizawl dataset, respectively. The performance of the LSTM model significantly improves as the zero values in the observed data are eliminated after reconstruction by MSSA. This enables the model to discern essential patterns and relationships in the data, which leads to more accurate forecasts. However, the hybrid model underestimates the rainfall, which can be tackled by hypertuning the parameters. The study highlights the importance of considering the interplay between rainfall and meteorological parameters for accurate rainfall forecasting in urban areas. The proposed MSSA–LSTM model can be used as a decision support tool for urban planning and disaster management.Significance StatementThis study addresses a critical need in multistep-ahead hourly rainfall forecasting in urban areas, with a focus on the unique conditions of northeast India. Our research has identified the presence of red noise in the rainfall data, shedding light on the complexities of the underlying rainfall patterns. Furthermore, we delve into the intricate interplay between rainfall and meteorological parameters, providing valuable insights into the factors influencing rainfall dynamics. Notably, our study underscores the region-specific challenges in rainfall forecasting. While the hybrid model demonstrates reasonable accuracy in the plains, its performance in the hilly region falls short of expectations. This highlights the nuanced nature of rainfall prediction in areas with varying topography and emphasizes the need for tailored forecasting approaches.

Khondaker, Md Murad Hossain; Momen, Mostafa

doi: 10.1175/jhm-d-23-0153.1pmid: N/A

AbstractHurricanes have been the most destructive and expensive hydrometeorological event in U.S. 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 Research and Forecasting (WRF) atmospheric model is coupled with the WRF hydrological (WRF-Hydro) model to simulate four major hurricanes landfalling in three hurricane-prone regions in the United States. First, a stepwise calibration is carried out in WRF-Hydro, which remarkably reduces streamflow forecast errors compared to the U.S. Geological Survey (USGS) gauges. Then, 60 coupled hydrometeorological 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 overdissipative 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.Significance StatementDespite significant recent improvements, numerical weather prediction models struggle to accurately forecast hurricane intensity and track due to many reasons such as inaccurate physical parameterization for hurricane flows. Furthermore, the performance of existing physics schemes is not well studied for hurricane flood forecasting. This study bridges these knowledge gaps by extensively evaluating different physical parameterizations for hurricane track, intensity, and flood forecasts using an atmospheric model coupled with a hydrological model. Then, a reduced diffusion boundary layer scheme is developed, making remarkable improvements in hurricane intensity forecasts due to the overdissipative nature of the considered schemes for major hurricane simulations. This reduced diffusion model is shown to significantly enhance hurricane flood forecasts, indicating the significance of hurricane dynamics on its induced precipitation.