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Pedram Darbandsari and Paulin Coulibaly

Abstract

Bayesian Model Averaging (BMA) is a popular ensemble-based post-processing approach where the weighted average of the individual members is used to generate predictive forecasts. As the BMA formulation is based on the law of total probability, possessing the ensemble of forecasts with mutually exclusive and collectively exhaustive properties is one of the main BMA inherent assumptions. Trying to meet these requirements led to the entropy-based BMA (En-BMA) approach. En-BMA uses the entropy-based selection procedure to construct an ensemble of forecasts with the aforementioned characteristics before the BMA implementation. This study aims at investigating the potential of the En-BMA approach for post-processing precipitation forecasts. Some modifications are proposed to make the method more suitable for precipitation forecasting. Considering the 6-hour accumulated precipitation forecasts with lead times of 6 to 24 hours from seven different models, we evaluate the effects of the proposed modifications and comprehensively compare the probabilistic forecasts, derived from the BMA and the modified En-BMA methods in two different watersheds. The results, in general, indicate the advantage of implementing the proposed modifications in the En-BMA structure for possessing more accurate precipitation forecasts. Moreover, the advantage of the modified En-BMA method over BMA in generating predictive precipitation forecasts is demonstrated based on different performance criteria in both watersheds and all forecasting horizons. These outperforming results of the modified En-BMA are more pronounced for large precipitation values, which are particularly important for hydrologic forecasting.

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Yaling Chen, Jun Wen, Rong Liu, Juan Zhou, and Wenhui Liu

Abstract

Precipitation is one of the most important meteorological factors affecting the water cycle and ecological system over the Source Region of the Three-River (SRTR), where the Yangtze River, Yellow River, and Lantsang River originated. The characteristics of water vapor transport and budget in annual and summer over the SRTR are analyzed using monthly observational and reanalysis datasets during 1980-2019. The linkage between water vapor transport and summer precipitation is also explored in this study. The results show that the Global Precipitation Climatology Project (GPCP) data are in agreement with the measured precipitation well. The SRTR is a sink region for water vapor, where the water vapor content shows an increasing trend with a rate of 0.2 mm/10a in annual and 0.3 mm/10a in summer. The water vapor mainly flows into the SRTR from the lower (521.2×106 kg s−1) and the middle (195.7×106 kg s−1) layers of the southern boundary in summer, while it exports from the middle (208.1×106 kg s−1) layer of the eastern boundary. The abnormal wind convergence and the low-pressure system, combining with the effects of the Western Pacific Subtropical High and the Mongolian High, provide conditions for the transport of water vapor and precipitation over the SRTR. A close relationship is found between water vapor flux and precipitation from the Singular Value Decomposition (SVD) analysis. The Brahmaputra River basin is the key region of water vapor transport over the SRTR, which contributes to further understanding the mechanisms of water vapor transport and the regional water cycle.

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Eunkyo Seo and Paul A. Dirmeyer

Abstract

Models have historically been the source of global soil moisture (SM) analyses and estimates of land-atmosphere coupling, even though they are usually calibrated and validated only locally. Satellite-based analyses have grown in fidelity and duration, offering an independent observationally-based alternative. However, satellite-retrieved SM time series include random and periodic errors that degrade estimates of land-atmosphere coupling, including correlations with other variables. This study proposes a mathematical approach to adjust daily time series of the European Space Agency (ESA) Climate Change Initiative (CCI) satellite SM product using information from physical-based land surface model (LSM) datasets using a Fourier transform time-filtering method to match the temporal power spectra locally to the LSMs, which tend to agree well with in situ observations.

When the original and time-filtered SM products are evaluated against ground-based SM measurements over the conterminous U.S., Europe, and Australia, results show the filtered SM has significantly improved subseasonal variability. The skill of the time-filtered SM is increased in temporal correlation by ∼0.05 over all analysis domains without introducing spurious regional patterns, affirming the stochastic nature of noise in satellite estimates, and skill improvement is found for nearly all land cover classes, especially savannas and grassland. Autocorrelation-based soil moisture memory (SMM), and the derived random component of soil moisture error (SME) are used to investigate the improvement of SM features. Time filtering reduces the random noise from the satellite-based SM product that is not explainable by physically-based SM dynamics; SME is usually diminished and the increased SMM is generally statistically significant.

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Albenis Pérez-Alarcón, Rogert Sorí, José C. Fernández-Alvarez, Raquel Nieto, and Luis Gimeno

Abstract

In this study, we identified the origin of the moisture associated with the tropical cyclones (TCs) precipitation in the North Atlantic Ocean basin during their three well-differentiated life stages between 1980 and 2018. The HURDAT2 database was used to detect the location of 598 TCs during their genesis, maximum intensification peak, and dissipation phases. The global outputs of the Lagrangian FLEXPART model were then used to determine the moisture sources. Using a K-means cluster analysis technique, seven different regions were identified as the most common locations for the genesis and maximum intensity of the TC phases, while six regions were found for the dissipation points. Our results showed that the origin of moisture precipitating was not entirely local over the areas of TC occurrence. The North Atlantic Ocean to the north of the Inter Tropical Convergence Zone at 10°N (NATL) -especially from tropical latitudes, the Caribbean Sea and the Gulf of Mexico- provides most of the moisture for TCs (∼87%). The Atlantic Ocean basin southward the ITCZ (SATL) played a non-negligible role (∼11%), with its contribution being most pronounced during the TC genesis phase, while the eastern tropical Pacific Ocean made the smallest contribution (∼2%). The moisture supported by TCs varied depending on their category, being higher for hurricanes than for major hurricanes or tropical storms. Additionally, the approach permitted to estimate the mean residence time of the water vapour uptake that produce the precipitation during TC activity, which ranged between 2.6 and 2.9 days.

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M.A. Stern, L.E. Flint, A.L. Flint, R.M. Boynton, J.A.E. Stewart, J.W. Wright, and J.H. Thorne

Abstract

High-resolution historical climate grids are readily available and frequently used as inputs for a wide range of regional management and risk assessments including water supply, ecological processes, and as baseline for climate change impact studies that compare them to future projected conditions. Because historical gridded climates are produced using various methods, their portrayal of landscape conditions differ, which becomes a source of uncertainty when they are applied to subsequent analyses. Here we tested the range of values from five gridded climate datasets. We compared their values to observations from 1,231 weather stations, first using each dataset’s native scale, and then after each was rescaled to 270-meter resolution. We inputted the downscaled grids to a mechanistic hydrology model and assessed the spatial results of six hydrological variables across California, in 10 ecoregions and 11 large watersheds in the Sierra Nevada. PRISM was most accurate for precipitation, ClimateNA for maximum temperature, and TopoWx for minimum temperature. The single most accurate dataset overall was PRISM due to the best performance for precipitation and low air temperature errors. Hydrological differences ranged up to 70% of the average monthly streamflow with an average of 35% disagreement for all months derived from different historical climate maps. Large differences in minimum air temperature data produced differences in modeled actual evapotranspiration, snowpack, and streamflow. Areas with the highest variability in climate data, including the Sierra Nevada and Klamath Mountains ecoregions, also had the largest spread for Snow Water Equivalent (SWE), recharge and runoff.

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Melissa L. Wrzesien, Sujay Kumar, Carrie Vuyovich, Ethan D. Gutmann, Rhae Sung Kim, Barton A. Forman, Michael Durand, Mark S. Raleigh, Ryan Webb, and Paul Houser

Abstract

Snow is a fundamental component of global and regional water budgets, particularly in mountainous areas and regions downstream that rely on snowmelt for water resources. Land surface models (LSMs) are commonly used to develop spatially distributed estimates of snow water equivalent (SWE) and runoff. However, LSMs are limited by uncertainties in model physics and parameters, among other factors. In this study, we describe the use of model calibration tools to improve snow simulations within the Noah-MP LSM as the first step in an Observing System Simulation Experiment (OSSE). Noah-MP is calibrated against the University of Arizona (UA) SWE product over a Western Colorado domain. With spatially varying calibrated parameters, we run calibrated and default Noah-MP simulations for water years 2010-2020. By evaluating both simulations against the UA dataset, we show that calibration decreases domain averaged temporal RMSE and bias for snow depth from 0.15 to 0.13 m and from -0.036 to -0.0023 m, respectively, and improves the timing of snow ablation. Increased snow simulation performance also improves estimates of model-simulated runoff in four of six study basins, though only one has statistically significant improvement. Spatially distributed Noah-MP snow parameters perform better than default uniform values. We demonstrate that calibrating variables related to snow albedo calculations and rain-snow partitioning, among other processes, is a necessary step for creating a nature run that reasonably approximates true snow conditions for the OSSEs. Additionally, the inclusion of a snowfall scaling term can address biases in precipitation from meteorological forcing datasets, further improving the utility of LSMs for generating reliable spatiotemporal estimates of snow.

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Ellen Eckert, David Hudak, Éva Mekis, Peter Rodriguez, Bo Zhao, Zen Mariani, Stella Melo, Kimberly Strong, and Kaley A. Walker

Abstract

To assess the performance of the most recent versions of the Global Precipitation Measurement (GPM) Integrated Multi-satellitE Retrievals for GPM (IMERG), namely V05 and V06, in Arctic regions, comparisons with Environment and Climate Change Canada (ECCC) Climate Network stations north of 60°N were performed. This study focuses on the IMERG monthly final products. The mean bias and mean error-weighted bias were assessed in comparison with twenty-five precipitation gauge measurements at ECCC Climate Network stations. The results of this study indicate that IMERG generally detects higher precipitation rates in the Canadian Arctic than ground-based gauge instruments, with differences ranging up to 0.05 mm h−1 and 0.04 mm h−1 for the mean bias and the mean error-weighted bias, respectively. Both IMERG versions perform similarly, except for a few stations, where V06 tends agree slightly better with ground-based measurements. IMERG’s tendency to detect more precipitation is in good agreement with findings indicating that weighing gauge measurement suffer from wind undercatch and other impairing factors, leading to lower precipitation estimates. Biases between IMERG and ground-based stations were found to be slightly larger during summer and fall, which is likely related to the increased precipitation rates during these seasons. Correlations of both versions of IMERG with the ground-based measurements are considerably lower in winter and spring than during summer and fall, which might be linked to issues that Passive Microwave (PMW) sensors encounter over ice and snow. However, high correlation coefficients with medians of 0.75-0.8 during summer and fall are very encouraging for potential future applications.

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Mahmoud Osman, Benjamin F. Zaitchik, Hamada S. Badr, Jason Otkin, Yafang Zhong, David Lorenz, Martha Anderson, Trevor F. Keenan, David L. Miller, Christopher Hain, and Thomas Holmes

Abstract

Recent years have seen growing appreciation that rapidly intensifying “flash droughts” are significant climate hazards with major economic and ecological impacts. This has motivated efforts to inventory, monitor, and forecast flash drought events. Here we consider the question of whether the term “flash drought” comprises multiple distinct classes of event, which would imply that understanding and forecasting flash droughts might require more than one framework. To do this, we first extend and evaluate a soil moisture volatility-based flash drought definition that we introduced in previous work and use it to inventory the onset dates and severity of flash droughts across the Contiguous United States (CONUS) for the period 1979-2018. Using this inventory, we examine meteorological and land surface conditions associated with flash drought onset and recovery. These same meteorological and land surface conditions are then used to classify the flash droughts based on precursor conditions that may represent predictable drivers of the event. We find that distinct classes of flash drought can be diagnosed in the event inventory. Specifically, we describe three classes of flash drought: “dry and demanding” events for which antecedent evaporative demand is high and soil moisture is low, “evaporative” events with more modest antecedent evaporative demand and soil moisture anomalies, but positive antecedent evaporative anomalies, and “stealth” flash droughts, which are different from the other two classes in that precursor meteorological anomalies are modest relative to the other classes. The three classes exhibit somewhat different geographic and seasonal distributions. We conclude that soil moisture “flash droughts” are indeed a composite of distinct types of rapidly intensifying droughts, and that flash drought analyses and forecasts would benefit from approaches that recognize the existence of multiple phenomenological pathways.

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Scott Steinschneider and Nasser Najibi

Abstract

This study investigates how extreme precipitation scales with dew point temperature across the Northeast U.S., both in the observational record (1948-2020) and in a set of downscaled climate projections in the state of Massachusetts (2006-2099). Spatiotemporal relationships between dew point temperature and extreme precipitation are assessed, and extreme precipitation – temperature scaling rates are evaluated on annual and seasonal scales using non-stationary extreme value analysis for annual maxima and partial duration series, respectively. A hierarchical Bayesian model is then developed to partially pool data across sites and estimate regional scaling rates, with uncertainty. Based on the observations, the estimated annual scaling rate is 5.5% per °C, but this varies by season, with most non-zero scaling rates in summer and fall and the largest rates (∼7.3% per °C) in the summer. Dew point temperatures and extreme precipitation also exhibit the most consistent regional relationships in the summer and fall. Downscaled climate projections exhibited different scaling rates compared to the observations, ranging between -2.5 and 6.2% per °C at an annual scale. These scaling rates are related to the consistency between trends in projected precipitation and dew point temperature over the 21st century. At the seasonal scale, climate models project larger scaling rates for the winter compared to the observations (1.6% per °C). Overall, the observations suggest that extreme daily precipitation in the Northeast U.S. only thermodynamic scales with dew point temperature in the warm season, but climate projections indicate some degree of scaling is possible in the cold season under warming.

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Yaoming Song, Anning Huang, and Haishan Chen

Abstract

Soil temperature (ST) is one of the key variables in land-atmosphere interactions. The response of ST to atmospheric changes and subsequent influence of ST on atmosphere can be recognized as the processes of signals propagation. Understanding the storing and releasing of atmosphere signals in ST favors the improvement of climate prediction and weather forecast. However the current understanding of the lagging response of ST to atmospheric changes is very insufficient. The analysis based on observation shows that both the storage of air temperature signals in deep ST even after four months and the storage of precipitation signals in shallow ST after one month are widespread phenomena in China. Air temperature signals at 2m can propagate to the soil depths of 160 cm and 320 cm after 1 month and 2 months, respectively. The storage of antecedent air temperature and precipitation signals in ST is slightly weaker and stronger during April to September, respectively, which is related to more precipitation during growing season. The precipitation signals in ST rapidly weaken after 2 months. Moreover, the effects of accumulated precipitation and air temperature on the signal storage in ST have significant monthly variations and vary linearly with soil depth and latitude. The storage of antecedent air temperature or precipitation signals in ST exhibits an obvious decadal variation with a period of more than 50 years, and it may be resulted from the modulation of the global climate patterns which largely affect local air temperature and precipitation.

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